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Integrating Deep Transfer Learning and Image Enhancement for Enhancing Defective Photovoltaic Cells Classification in Electroluminescence Images 2 2 The rapid growth of photovoltaic (PV) systems has highlighted the need for efficient and reliable defect detection to maintain system performance. Electroluminescence (EL) imaging has emerged as a promising technique for identifying defects in PV cells; however, challenges remain in accurately classifying defects due to the variability in image quality and the complex nature of the defects. Existing studies often focus on single image enhancement techniques or fail to comprehensively compare the performance of various image enhancement methods across different deep learning (DL) models. This research addresses these gaps by proposing an in-depth analysis of the impact of multiple image enhancement techniques on defect detection performance, using various deep learning models of low, medium, and high complexity. The results demonstrate that mid-complexity models, especially DarkNet-53, achieve the highest performance with an accuracy of 94.55% after MSR2 enhancement. DarkNet-53 consistently outperformed both lower-complexity models and higher-complexity models in terms of accuracy, precision, and F1-score. The findings highlight that medium-depth models, enhanced with MSR2, offer the most reliable results for photovoltaic defect detection, demonstrating a significant improvement over other models in terms of accuracy and efficiency. This research provides valuable insights for optimizing defect detection systems in photovoltaic applications, emphasizing the importance of both model complexity and image enhancement techniques for robust performance. 1 15 2024/12/01 1403/9/11 2025/02/18 1403/11/30 Hanim Suraya Mohd Mokhtar Hanim Suraya Mohd Mokhtar Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia hanimsuraya@studentmail.unimap.edu.my. Aimi Salihah Abdul Nasir Aimi Salihah Abdul Nasir Centre of Excellence for Renewable Energy (CERE), Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia aimisalihah@unimap.edu.my Mohammad Faridun Naim Tajuddin Mohammad Faridun Naim Tajuddin Centre of Excellence for Renewable Energy (CERE), Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia faridun@unimap.edu.my Muhammad Hafeez Abdul Nasir Muhammad Hafeez Abdul Nasir School of Housing, Building and Planning, Universiti Sains Malaysia (USM), 11700 Gelugor, Pulau Pinang, Malaysia. Malaysia hafeeznasir@usm.my Kumuthawathe Ananda Rao Kumuthawathe Ananda Rao Centre of Excellence for Renewable Energy (CERE), Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia kumuthawathe@unimap.edu.my Photovoltaic (PV) Defect Classification Electroluminescence Multi-Scale Retinex (MSR) Multi-Scale Retinex 2 (MSR2) Pre-Trained Models. [1] B. Su, H. Chen, Y. Zhu, W. Liu, and K. Liu, “Classification of Manufacturing Defects in Multicrystalline Solar Cells with Novel Feature Descriptor,” IEEE Trans. Instrum. Meas., vol. 68, no. 12, pp. 4675–4688, 2019, doi: 10.1109/TIM.2019.2900961.##[2] A. Zotin and A. Zotin, “Fast Algorithm of Image Enhancement based on Multi-Scale Fast Algorithm of Image Enhancement based on Multi-Scale Retinex Retinex,” Procedia Comput. Sci., vol. 131, pp. 6–14, 2018, doi: 10.1016/j.procs.2018.04.179.##[3] C. Lee, J. Shih, and C. Lien, “Adaptive Multiscale Retinex for Image Contrast Enhancement,” 2013 Int. Conf. Signal-Image Technol. Internet-Based Syst., pp. 43–50, 2013, doi: 10.1109/SITIS.2013.19.##[4] S. Fan et al., “A novel image enhancement algorithm to determine the dust level on photovoltaic ( PV ) panels,” Renew. Energy, vol. 201, no. P1, pp. 172–180, 2022, doi: 10.1016/j.renene.2022.10.073.##[5] Z. Meng, S. Xu, L. Wang, Y. Gong, X. Zhang, and Y. Zhao, “Defect object detection algorithm for electroluminescence image defects of photovoltaic modules based on deep learning,” Energy Sci. Eng., vol. 10, no. 3, pp. 800–813, 2022, doi: 10.1002/ese3.1056.##[6] I. Zyout and A. Oatawneh, “Detection of PV Solar Panel Surface Defects using Transfer Learning of the Deep Convolutional Neural Networks,” 2020 Adv. Sci. Eng. Technol. Int. Conf. ASET 2020, 2020, doi: 10.1109/ASET48392.2020.9118382.##[7] H. Li, “Research on Surface Defect Detection of Solar PV Panels Based on Pre-Training Network and Feature Fusion,” IOP Conf. Ser. Earth Environ. Sci., 2021, doi: 10.1088/1755-1315/651/2/022071.##[8] S. Deitsch et al., “Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images,” Sol. Energy, vol. 185, no. July, pp. 455–468, 2019, doi: 10.1016/j.solener.2019.02.067.##[9] M. W. Akram et al., “CNN based Automatic Detection of Photovoltaic Cell Defects in Electroluminescence Images,” Energy, vol. 189, p. 116319, 2019, doi: 10.1016/j.energy.2019.116319.##[10] A. M. Karimi et al., “Automated Pipeline for Photovoltaic Module Electroluminescence Image Processing and Degradation Feature Classification,” IEEE J. Photovoltaics, vol. 9, no. 5, pp. 1324–1335, 2019, doi: 10.1109/JPHOTOV.2019.2920732.##[11] M. Mayr, M. Hoffmann, A. Maier, and V. Christlein, “Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm,” in 2019 IEEE International Conference on Image Processing (ICIP), 2019, pp. 1885–1889.##[12] J. Fioresi et al., “Automated Defect Detection and Localization in Photovoltaic Cells using Semantic Segmentation of Electroluminescence Images,” IEEE J. Photovoltaics, vol. 12, no. 1, pp. 53–61, 2021.##[13] C. J. Brabec et al., “A Benchmark for Visual Identification of Defective Solar Cells in Electroluminescence Imagery,” 2018. [Online]. Available: https://api.semanticscholar.org/CorpusID:215885514##[14] S. Deitsch et al., “Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images,” Mach. Vis. Appl., vol. 32, no. 4, pp. 1–23, 2021, doi: 10.1007/s00138-021-01191-9.##[15] B. Su, H. Chen, Y. Zhu, W. Liu, and K. Liu, “Classification of Manufacturing Defects in Multicrystalline Solar Cells With Novel Feature Descriptor,” IEEE Trans. Instrum. Meas., vol. 68, no. 12, pp. 4675–4688, 2019, doi: 10.1109/TIM.2019.2900961.##[16] B. Su, Z. Zhou, and H. Chen, “PVEL-AD: A Large-Scale Open-World Dataset for Photovoltaic Cell Anomaly Detection,” IEEE Trans. Ind. Informatics, vol. 19, no. 1, pp. 404–413, 2023, doi: 10.1109/TII.2022.3162846.##[17] B. Su, H. Chen, and Z. Zhou, “BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell Defect Detection,” IEEE Trans. Ind. Electron., vol. PP, p. 1, 2021, doi: 10.1109/TIE.2021.3070507.##[18] B. Su, H. Chen, P. Chen, G.-B. Bian, kun Liu, and W. Liu, “Deep Learning-Based Solar-Cell Manufacturing Defect Detection With Complementary Attention Network,” IEEE Trans. Ind. Informatics, vol. PP, p. 1, 2020, doi: 10.1109/TII.2020.3008021.##[19] F. N. Shaari, “Deep CNN-LSTM Network Integration for COVID- 19 Classification,” 2023 IEEE 2nd Natl. Biomed. Eng. Conf., pp. 142–147, 2023, doi: 10.1109/NBEC58134.2023.10352632.##[20] J. Zhou, J. Yao, W. Zhang, and D. Zhang, “Multi-Scale Retinex-Based Adaptive Gray-Scale Transformation Method for Underwater Image Enhancement,” Multimed. Tools Appl., vol. 81, no. 2, pp. 1811–1831, 2022, doi: 10.1007/s11042-021-11327-8.##[21] M. Ali et al., “Pneumonia Detection Using Chest Radiographs with Novel EfficientNetV2L Model,” IEEE Access, vol. 12, no. February, pp. 34691–34707, 2024, doi: 10.1109/ACCESS.2024.3372588.##[22] S. I. Safie et al., “Comparison of SqueezeNet and DarkNet-53 based YOLO-V3 Performance for Beehive Intelligent Monitoring System,” 2023 IEEE 13th Symp. Comput. Appl. Ind. Electron., pp. 62–65, 2023, doi: 10.1109/ISCAIE57739.2023.10165285.##[23] A. Sameerunnisa, “Brain Tumor Classification using EfficientNet-B0 Model,” 2022 2nd Int. Conf. Adv. Comput. Innov. Technol. Eng., pp. 2503–2509, 2022, doi: 10.1109/ICACITE53722.2022.9823526.##[24] C. Szegedy et al., “Going Deeper with Convolutions,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June, pp. 1–9, 2015, doi: 10.1109/CVPR.2015.7298594.##[25] H. Wang, F. Zhang, and L. Wang, “Fruit Classification Model Based on Improved Darknet53 Convolutional Neural Network,” pp. 881–884, 2020, doi: 10.1109/ICITBS49701.2020.00194.##[26] F. N. Shaari, A. Salihah, A. Nasir, and W. A. Mustafa, “Variant Histogram Equalization based Enhancement to Transfer Learning in Detection of COVID-19 Chest X-Ray Images,” 2023 IEEE 2nd Natl. Biomed. Eng. Conf., pp. 158–163, 2023, doi: 10.1109/NBEC58134.2023.10352580.##[27] T. B. Beijing, T. B. Beijing, T. B. Beijing, and J. 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Vacuum-based Robotic Gripper using Vacuum Generator and Soft Suction Cup for Pick-and-Place of Electronic PCB Boards 2 2 The automation of Printed Circuit Board (PCB) assembly using robotic arms is increasingly essential in the electronics manufacturing industry, driven by the need for high precision and efficiency. A significant challenge in this process is the delicate handling and accurate placement of various types of PCB boards, such as SATA M.2, mSATA, and SATA Slim. This research aims to design and evaluate a vacuum-based robotic gripper using a vacuum generator and soft suction cup for the pick-and-place operations of electronic PCB boards. The methodology involves the design, fabrication, and experimental testing of the vacuum gripper, analyzing its performance across different feed pressures and vacuum levels. The principal results show that the vacuum gripper is highly effective in securely handling different PCB types, with success rates improving significantly at higher feed pressures, particularly at 0.3 MPa where all three PCB types attained perfect success rates of 100%. Specifically, the vacuum flow rates at a vacuum level of 80 kPa were 0.0010 NL/s, 0.002 NL/s, and 0.0030 NL/s for feed pressures of 0.1 MPa, 0.2 MPa, and 0.3 MPa, respectively. These findings confirm the vacuum gripper's capability to enhance automation in PCB assembly, offering a scalable and adaptable solution that meets the industry's demands for precision, efficiency, and reliability. Overall, the vacuum gripper demonstrated a 100% success rate for all tested PCB types at optimal feed pressure, significantly improving. This study provides a foundation for future improvements in robotic handling systems for delicate electronic components. 16 26 2024/12/012024/12/05 1403/9/15 2025/02/182025/02/20 1403/12/2 Muhammad Syafiq Sheik Azmi Muhammad Syafiq Sheik Azmi Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia. Malaysia muhammadsyafiq@studentmail.unimap.edu.my Muhamad Hisyam Rosle Muhamad Hisyam Rosle Department of Mechanical Precision Engineering, Malaysia-Japan International Institute of Technology, Malaysia. Malaysia m.hisyam@utm.my Muhammad Nazrin Shah Shahrol Aman Muhammad Nazrin Shah Shahrol Aman Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia. Malaysia nazrinshahrol@unimap.edu.my Ali Akbar Abd Aziz Ali Akbar Abd Aziz Department of Engineering, SMART Modular Technologies Sdn Bhd, Malaysia. Malaysia abdaziz.aliakhbar@smartm.com Chandran Tetegre Chandran Tetegre Department of Engineering, SMART Modular Technologies Sdn Bhd, Malaysia. Malaysia chandran.tategre@smartm.com Vacuum-based robotic gripper PCB assembly automation pick-and-place operations electronic components handling. [1] O. Rabinovich and A. Epstein, "Analytical Design of Printed Circuit Board (PCB) Metagratings for Perfect Anomalous Reflection," IEEE Transactions on Antennas and Propagation, vol. 66, no. 8, pp. 4086–4095, Aug. 2018, doi: https://doi.org/10.1109/tap. 2018 .2836379. ##[2] Y. Lou and S. Hua, "Research on Key Technologies of Robotic Assembly," Applied Mechanics and Materials, vol. 101–102, 2011, doi: https://doi.org/10.4028/www.scientific.net/AMM.101-102.1051.##[3] B. Chen, J. Wan, L. Shu, P. Li, M. Mukherjee, and B. Yin, "Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges," IEEE Access, vol. 6, pp. 6505–6519, Jan. 2018, doi: https://doi.org/10.1109/access.2017.2783682.##[4] H. Khan, S. J. Abbasi, M. Salman, and M. C. Lee, "Super Twisting Sliding Mode Control-Based Impedance Control for Robot Arm End-Effector Force Tracking," in Proc. 61st Annual Conf. Society of Instrument and Control Engineers (SICE), Sep. 2022, doi: https://doi.org/10.23919/sice56594.2022.9905784.##[5] B. Micieta, P. Macek, V. Binasova, L. Dulina, M. Gaso, and J. Zuzik, "Modular Intelligent Control System in the Pre-Assembly Stage," Electronics, vol. 13, no. 9, p. 1609, 2024, doi: https://doi.org/10.3390 /electronics 13091609.##[6] H. Fakhurldeen, F. Dailami, and A. Pipe, "CARA System Architecture—A Click and Assemble Robotic Assembly System," in Proc. IEEE Int. Conf. Robotics and Automation (ICRA), 2019, doi: https://doi.org/ 10.1109/ICRA.2019.8794114.##[7] D. A. Medina Portilla and V. Nandikolla, "Design of a Dexterous Robotic Arm Manipulator Using Hybrid BCI," in ASME Int. Mechanical Engineering Congress and Exposition, 2020, doi: https://doi.org/10.1115/1.000 4180V.##[8] B. Cianciotto, D. Price, L. Spencer, M. Garcia, and A. Tekes, "Design and Development of a Novel Soft Gripper Manipulated by a Robotic Arm," in ASME Int. Mechanical Engineering Congress and Exposition, 2021, doi: https://doi.org/10.1115/IMECE2021-69880.##[9] T. J. Cairnes, C. J. Ford, E. Psomopoulou, and N. Lepora, "An Overview of Robotic Grippers," IEEE Potentials, 2023, doi: https://doi.org/10.1109/MPOT. 2023.3236143.##[10] R. Gutierrez, M. Garcia, J. McDuffie, C. Long, and A. Tekes, "Development of Wire Actuated Monolithic Soft Gripper Positioned by Robot Manipulator," in ASME Dynamic Systems and Control Conf., 2020, doi: https://doi.org/10.1115/DSCC2020-3198.##[11] W. Afzal, S. Iqbal, Z. Tahira, and M. Qureshi, "Gesture Control Robotic Arm Using Flex Sensor," Automation, Control and Intelligent Systems, vol. 6, no. 4, 2017, doi: https://doi.org/10.11648/j.acm.20170604.1 2.##[12] B. Varghese and B. Thilagavathi, "Design and Wireless Control of Anthropomorphic Robotic Arm," in Proc. IEEE Int. Conf. Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015, doi: https://doi.org/10.1109/ICIIECS.2015.71929 75.##[13] K. Sekarsari, D. Ikhsan, and Marfin, "Design of 2 DOF Arm Robot Control System Using Ultrasonic Sensor," IOP Conf. Ser.: Materials Science and Engineering, vol. 550, 2019, doi: https://doi.org/10.1088 /1757-899X/550/1/012014.##[14] Y. Yamamoto, S. Wakimoto, T. Kanda, and D. Yamaguchi, "A Soft Robot Arm with Flexible Sensors for Master–Slave Operation," in Proc. 8th Int. Electronic Conf. Sensors and Applications (ECSA), 2021, doi: https://doi.org/10.3390/ecsa-8-11311.##[15] H. Götz, A. Santarossa, A. Sack, T. Pöschel, and P. Müller, "Soft Particles Reinforce Robotic Grippers: Robotic Grippers Based on Granular Jamming of Soft Particles," Granular Matter, 2021, doi: https://doi.org/ 10.1007/s10035-021-01193-4.##[16] C. Davenport, F. Parietti, and H. Asada, "Design and Biomechanical Analysis of Supernumerary Robotic Limbs," in ASME Dynamic Systems and Control Conf., 2012, doi: https://doi.org/10.1115/DSCC 2012-MOVIC 2012-8790.##[17] Yu and F. Chen, "Inverse Kinematic Solution of 6-DOF Robot-Arm Based on Dual Quaternions and Axis Invariant Methods," Arabian J. for Science and Engineering, vol. 47, no. 12, pp. 15915–15930, 2022, doi: https://doi.org/10.1007/s13369-022-06794-6.##[18] S. Leder, H. Kim, M. Sitti, and A. Menges, "Enhanced Co-design and Evaluation of a Collective Robotic Construction System for the Assembly of Large-scale In-plane Timber Structures," Automation in Construction, vol. 162, p. 105390, June 2024, doi: https://doi.org/10.1016/j.autcon.2024.105390.##[19] V. M. S. Reddy, Y. N. K. Reddy, R. Manideep, K. S. Kumar, U. Srivastav, and M. Kalpana, "Design and Fabrication of 3D-Printed Robotic Arm by Using Stepper Motor," Int. J. Research in Applied Science and Engineering Technology, vol. 12, no. 4, pp. 1556–1565, 2024, doi: https://doi.org/10.22214/ijraset.2024.60124.##[20] Y. Wen, C. Chen, Z. Lyu, Y. Liang, and Z. Zhang, "Design and Application of Bidirectional Soft Actuator With Multiangle Chambers," Industrial Robot, July 2024, doi: https://doi.org/10.1108/ir-04-2024-0136.##[21] B. Xie, M. Jin, J. Duan, Z. Li, W. Wang, M. Qu, and Z. Yang, "Design of Adaptive Grippers for Fruit-Picking Robots Considering Contact Behavior," Agriculture, vol. 14, no. 7, p. 1082, 2024, doi: https://doi.org/10.3390/agriculture14071082.##[22] D. Diachenko, A. Partyshev, S. Pizzagalli, Y. Bondarenko, T. Otto, and V. Kuts, "Industrial Collaborative Robot Digital Twin Integration and Control Using Robot Operating System," J. Machine Engineering, Apr. 2022, doi: https://doi.org/10.36897 /jme/148110.##[23] B. S. Seibel, "Vacuum Pumps: Direct Control of Vacuum and Grip," in CRC Press eBooks, pp. 64–65, 2024, doi: https://doi.org/10.1201/9781003525639-24.##[24] J. Luo, X. Zhou, C. Zeng, Y. Jiang, W. Qi, K. Xiang, M. Pang, and B. Tang, "Robotics Perception and Control: Key Technologies and Applications," Micromachines, vol. 15, no. 4, p. 531, 2024, doi: https://doi.org/10.3390/mi15040531.##[25] C.-N. Wang, N.-L. Nhieu, and T.-A. Pham Viet, "Enhancing Efficiency in PCB Assembly for the Leading Global Electronics Manufacturing Services Firm: A TRIZ and Ant Colony Optimization Approach," Int. J. Advanced Manufacturing Technology, July 2024, doi: https://doi.org/10.1007 /s00170-024-14025-5.## ##

Variable Step-size Hill-Climbing Search (VS-HCS) MPPT Algorithm for Hydrokinetic Energy Harnessing 2 2 Hydrokinetic energy harnessing has emerged as a promising renewable energy that utilizes the kinetic energy of moving water to generate electricity. Nevertheless, the variation and fluctuation of water velocity and turbulence flow in a river is a challenging issue, especially in designing a control system that can harness the maximum output power with high efficiency. Besides, the conventional Hill-climbing Search (HCS) MPPT algorithm has weaknesses, such as slow tracking time and producing high steady-state oscillation, which reduces efficiency. In this paper, the Variable-Step Hill Climbing Search (VS-HCS) MPPT algorithm is proposed to solve the limitation of the conventional HCS MPPT. The model of hydrokinetic energy harnessing is developed using MATLAB/Simulink. The system consists of a water turbine, permanent magnet synchronous generator (PMSG), passive rectifier, and DC-DC boost converter. The results show that the power output achieves a 28 % increase over the system without MPPT and exhibits the lowest energy losses with a loss percentage of 0.9 %. 27 35 2024/12/012024/12/052024/12/05 1403/9/15 2025/02/182025/02/202025/02/23 1403/12/5 WAN ISMAIL IBRAHIM WAN ISMAIL IBRAHIM Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, FTKEE UMPSA, 26600 Pekan, Pahang, MALAYSIA. MALAYSIA wismail@umpsa.edu.my Nasiruddin Sadan Nasiruddin Sadan Department of Asset Development & Maintenance, Institut Latihan Perindustrian (ILP) Bukit Katil, Lot 4345, Jalan Tun Telani, Mukin Bukit Katil, Hang Tuah Jaya, 75450 Ayer Keroh Malacca, Malaysia. MALAYSIA nasiruddin.sadan@jtm.gov.my Noorlina Ramli Noorlina Ramli Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, FTKEE UMPSA, 26600 Pekan, Pahang, MALAYSIA. MALAYSIA norlina@umpsa.edu.my Mohd Riduwan Ghazali Riduwan Ghazali Mohd Riduwan Ghazali Riduwan Ghazali Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, FTKEE UMPSA, 26600 Pekan, Pahang, MALAYSIA. MALAYSIA riduwan@umpsa.edu.my Ilham Fuad Ilham Fuad Aku Munikasi Snd Bhd. 3 Tower,349 Jalan Ampang, kampung Berembang, 55000 Wilayah Persekutuan, Kuala Lumpur, Malaysia. MALAYSIA admin@akumunikasi.com.my Hill-Climbing Search Hydrokinetic Energy Harnessing MPPT Algorithm [1] P. Esmaeili, M. Rafei, M. Salari, and D. Balsalobre-Lorente, “From oil surges to renewable shifts: Unveiling the dynamic impact of supply and demand shocks in global crude oil market on U.S. clean energy trends,” Energy Policy, vol. 192, p. 114252, 2024.##[2] W. Przychodzen, “Political factors in renewable energy generation: Do populism, carbon tax and feed-in tariffs matter?,” Energy Res. Soc. Sci., vol. 115, p. 103628, 2024.##[3] X. Jin, B. Liu, S. Liao, C. Cheng, Y. Zhang, and Z. Jia, “Assessing hydropower capability for accommodating variable renewable energy considering peak shaving of multiple power grids,” Energy, vol. 305, p. 132283, 2024.##[4] F. Behrouzi, M. Nakisa, A. Maimun, and Y. M. Ahmed, “Global renewable energy and its potential in Malaysia: A review of Hydrokinetic turbine technology,” Renew. Sustain. Energy Rev., vol. 62, pp. 1270–1281, 2016.##[5] H. Wirawan and Y. M. L. 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Oguz, “Potential and prospects of hydrokinetic energy in Malaysia: A review,” Sustain. Energy Technol. Assessments, vol. 52, p. 102265, 2022.##[10] M. Kamran, J. Sajid, M. N. Sajid, M. M. Ahmad, M. A. Ismail, and M. B. Sajid, “Energy efficiency and greenhouse gas emission reduction potential of solar PV and biomass-based systems for a food processing plant,” Case Stud. Therm. Eng., vol. 60, p. 104674, 2024.##[11] B. Kirke, “Towards more cost-effective river hydrokinetic turbines,” Energy Sustain. Dev., vol. 78, p. 101370, 2024.##[12] P. K. Yadav, A. Kumar, and S. Jaiswal, “A critical review of technologies for harnessing the power from flowing water using a hydrokinetic turbine to fulfill the energy need,” Energy Reports, vol. 9, pp. 2102–2117, 2023.##[13] W. I. Ibrahim and M. R. Mohamed, “The Potential of Hydrokinetic Energy Harnessing in Pahang River Basin,” in The 12th National Technical Seminar on Unmanned System Technology 2020, 2020, pp. 1–13.##[14] M. Michas, C. E. Ugalde-Loo, W. Ming, N. Jenkins, and S. Runge, “Maximum power extraction from a hydrokinetic energy conversion system,” IET Renew. Power Gener., pp. 1411–1419, 2019.##[15] W. I. Ibrahim, M. R. Mohamed, and R. M. T. R. Ismail, “Direct Power Control Method of Maximum Power Point Tracking ( MPPT ) Algorithm for Pico-Hydrokinetic River Energy Conversion System,” in International Conference on Electrical, Electronics, and Computer Engineering (InECCE), 2020, pp. 691–703.##[16] N. Sadan and W. I. Ibrahim, “Hill Climbing Search (HCS) MPPT Algorithm for Hydrokinetic Energy Harnessing,” in Engineering Technology International Conference (ETIC 2022), 2022, pp. 1–6.##[17] W. I. Ibrahim et al., “Sensorless Fuzzy Logic Controller ( FLC ) based Maximum Power Point Tracking ( MPPT ) Algorithm for Hydrokinetic Energy Harnessing,” pp. 4–9.##[18] W. I. Ibrahim, M. R. Mohamed, R. Mohd, and T. Raja, “Hybrid HCS-Fuzzy MPPT Algorithm for Hydrokinetic Energy Harnessing,” in 2022 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS 2022), 2022, no. June, pp. 119–124.##[19] M. M. Rezaei, “A nonlinear maximum power point tracking technique for DFIG-based wind energy conversion systems,” Eng. Sci. Technol. an Int. J., vol. 21, no. 5, pp. 901–908, 2018.##[20] M. K. A. Kamarudin et al., “Hydrological and climate impacts on river characteristics of pahang river basin, Malaysia,” Heliyon, vol. 9, no. 11, p. e21573, 2023.##[21] W. I. Ibrahim, R. M. T. R. Ismail, and M. R. Mohamed, “Micro-Hydro Energy Estimation for Hydrokinetic Energy Harnessing at Sungai Lembing,” in Proceedings of the 10th National Technical Seminar on Underwater System Technology (Nusys 2018), 2019, vol. 538.##[22] W. I. Ibrahim, M. R. Mohamed, and R. M. T. R. Ismail, “Modelling of Power Curve Equation for Small-Scale Vertical Axis Hydrokinetic Turbine BT - Proceedings of the 6th International Conference on Electrical, Control and Computer Engineering,” 2022, pp. 57–67.##[23] L. Cavalari Labigalini, R. de V. Salvo, R. Sene de Lima, R. Corrêa da Silva, and I. de Marchi Neto, “Hydrokinetic turbine design through performance prediction and hybrid metaheuristic multi-objective optimization,” Energy Convers. Manag., vol. 238, p. 114169, 2021.##[24] M. A. Abdullah, A. H. M. Yatim, C. W. Tan, and A. S. Samosir, “Particle swarm optimization-based maximum power point tracking algorithm for wind energy conversion system,” PECon 2012 - 2012 IEEE Int. Conf. Power Energy, no. December, pp. 65–70, 2012.##[25] H. H. H. Mousa, A. Youssef, and E. E. M. Mohamed, “Electrical Power and Energy Systems Variable step size P & O MPPT algorithm for optimal power extraction of multi-phase PMSG based wind generation system,” Electr. 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Analysis of Flux Density Distribution Effects on Rotor Bar Models to Improve Energy Efficiency, Environmental Sustainability, and Economic Outcomes in Induction Motors 2 2 Induction motors are highly favored in industrial applications for their ease of operation, compactness, lightweight, efficiency, low maintenance, and cost-effectiveness. They are widely used in conveyors, compressors, crushers, drills, fans, escalators, refrigerators, and electric vehicles. In Malaysia, industrial motors account for about 48% of energy consumption. This research introduces an improved rotor design with optimized rotor bars. Using MotorSolve (IM) software and theoretical calculations, the study found that the new design boosts energy efficiency. The new rotor bar design achieved an energy efficiency of 76.92%, compared to 74% for the current design. In terms of energy efficiency, this research found that adopting high-efficiency motors in industrial applications can save a significant amount of energy. These motors can also be used in a variety of horsepower ranges. The research suggests a maintenance plan for malfunctioning motors that attempts to reduce energy consumption, motor losses, and CO2 emissions in any apparatus. These results offer valuable insights for policymakers to refine energy policies for induction motors. In the future, real-time estimation of the motor's actual operating loss will be required to properly predict the trend in motor efficiency loss under various failure scenarios, which is consistent with the research goal of reducing energy losses in induction motors. 36 45 2024/12/012024/12/052024/12/052024/12/06 1403/9/16 2025/02/182025/02/202025/02/232025/02/23 1403/12/5 YANAWATI YAHYA YANAWATI YAHYA Electrical Engineering Section, British Malaysian Institute, Universiti Kuala Lumpur, Gombak, 53100, MALAYSIA. MALAYSIA yanawati@unikl.edu.my Nor Shafiqin Shariffuddin Nor Shafiqin Shariffuddin PETRONAS FLOATING LNG 1 (L) LTD., Block B, Level, Lot R-8 Riverson Suites, Kota Kinabalu, 88100, MALAYSIA. MALAYSIA norshafiqin@unikl.edu.my Muhammad Khairul Hisyam Jarail Muhammad Khairul Hisyam Jarail PETRONAS FLOATING LNG 1 (L) LTD., Block B, Level, Lot R-8 Riverson Suites, Kota Kinabalu, 88100, MALAYSIA. MALAYSIA mkhairulhisyam.jarai@petronas.com Dina Maizana Dina Maizana Department Teknik Elektro, Universitas Medan Area, INDONESIA. INDONESIA maizanadina@gmail.com Ibrahim Alhamrouni Ibrahim Alhamrouni Electrical Engineering Section, British Malaysian Institute, Universiti Kuala Lumpur, Gombak, 53100, MALAYSIA. MALAYSIA ibrahim.mohamed@unikl.edu.my Mohd Khairil Rahmat Mohd Khairil Rahmat Electrical Engineering Section, British Malaysian Institute, Universiti Kuala Lumpur, Gombak, 53100, MALAYSIA. MALAYSIA mkhairil@unikl.edu.my Induction Motor Energy Efficiency Bar Type Bar Size Bar Conductivity Energy Saving. [1] Al-Oraini, D. S. B. I. (2010). Chapter 4 - Three Phase Induction Machines | PDF | Electric.##[2] Augie Hand. (2022). Three-Phase Induction Motor. In Electric Motor Maintenance and Troubleshooting (pp. 212 – 214). McGraw-Hill.##[3] Jerry R. Bednarczyk, P.E. (2023). Induction Motor Theory, 176. An Approved Continuing Education Provider.##[4] Daut, I., Anayet, K., Gomesh, N., Irwan, M., Asri, M., & Rashid, M. A. (2012). Development of 0.5 HP induction motor with reduced slots in rotor and stator core and its performance evaluation, 7(11), 2004–2011.##[5] Jimmie J. Cathey. (2001). Electric Machines, Analysis and Design Applying Matlab. McGraw-Hill Publishing Com. Lmt, pg 375-415. ##[6] Gupta, R., & Kumar, S. (2023). Advanced electrical machine design: Principles and applications. Springer. ##[7] Juha Pyrhonen, Tapani Jokinen, & Valeria Hrabovcova. (2008). Design Process and Properties of Rotating Electrical Machines. In Design of Rotating Electrical Machines (p. 334). John Wiley & Sons, Ltd. ##[8] Garcia, R., Lopez, M., & Fernandez, J. (2023). Advanced processing techniques for high-efficiency silicon steel laminations. Journal of Electrical Engineering Materials. ##[9] Kothari, D. P., & Nagrath, I. J. (2010). Electric Machines (Fourth). Mc Graw Hill.##[10] Kim, S., Park, J., & Lee, H. (2023). Analysis of friction and windage losses in high-speed electric motors. IEEE Transactions on Industrial Electronics.##[11] MotorSolve (IM) Software, Siemens. (2023). Simcenter Motorsolve user guide. Siemens Digital Industries Software.##[12] Ranganathan, G. (2009). Review On Efficiency Improvement In Squirrel Cage Induction Motor By Using Dcr Technology, 60(4), 227–236. Reviews- Letters- Reports. ISSN 1335-3632.##[13] Stephen L. Herman. (1999). Three Phase Motors. In Electrical Transformers & Rotating Machines (pp. 312 – 314). Delmar Publisher.##[14] VTU e-Learning Centre. (2016). Design of Rotor: Number of Slots. Retrieved January 23, 2016, from http://elearning.vtu.ac.in/16/ENotes/Elec.##[15] Yanawati, Y., Daut, I., Shafiqin, S. N., Pungut, I., Syatirah, M. N., Gomesh, N., Haidar, N. (2012). Thicknesses of Non-oriented Steel Sheets for 0.5 Hp Induction Motor, 2(2), 1–5.##[16] Yanawati, Y., & Maizana, D. (2014). Performance Comparison on 0.35 mm and 0.50 mm thicknesses of Non-oriented Steel Sheets Using FEM, The 4th International Malaysia-Ireland Joint Symposium on Engineering, Science, and Business 2014, IMiEJS2014, Penang Island, Malaysia, 25th - 26th June 2014. Oct 2014, Applied Mechanics and Materials 679:112-117.##[17] Marfoli, A.; Nardo, M.D.; Degano, M.; Gerada, C.; Chen, W. Rotor Design Optimization of Squirrel Cage Induction Motor—Part I: Problem Statement. IEEE Trans. Energy Convers. 2021, 36, 1271–1279. ##[18] Nardo, M.D.; Marfoli, A.; Degano, M.; Gerada, C.; Chen, W. Rotor Design Optimization of Squirrel Cage Induction Motor—Part II: Results Discussion. IEEE Trans. Energy Convers. 2021, 36, 1280–1288. ##[19] Um, D.Y.; Park, G.S. Determination Scheme of Stator Parameters for Making Rotating Fields Circular in a Single-Phase Induction Motor. IEEE Trans. Magn. 2020, 56, 1–5. ##[20] Chasiotis, I.D.; Karnavas, Y.L. A Novel Design Methodology for the Compliance of Single Phase Induction Motors with Recent Industrial Premium Efficiency Standards. Eng. Rep. 2020, 2, e12265.##[21] Ioannis D. Chasiotis, Yannis L. Karnavas, Franck Scuiller. “Effect of Rotor Bars Shape on the Single-Phase Induction Motors Performance: An Analysis toward Their Efficiency Improvement”, Energies 2022, 15(3), 717.## ##

Power Quality Issues on Jordan Wind Farm Connected to Grid System 2 2 This paper presents a comprehensive research endeavor focused on evaluating the influence of renewable energy, particularly wind power, on power quality within the context of Jordan's electrical grid. The escalating global demand for energy, coupled with the imperative to curb greenhouse gas emissions, has propelled the rapid adoption of renewable energy sources. Against this backdrop, the study aims to meticulously analyze the effects of wind energy projects on power quality parameters such as voltage fluctuations, harmonics, and power factor. Through an extensive methodology comprising data collection, rigorous analysis, and advanced simulation techniques, actionable insights are provided into the seamless integration of renewable energy into existing grid infrastructures. In this work, power quality parameters like Total Harmonic Distortion, flickers, power frequency, Crest factor, and voltage unbalance are measured at Al-Tafilah Governorate, Jordan. The significance of this study lies in its contribution to the development of strategies and guidelines essential for policymakers, engineers, and stakeholders. By fostering a deeper understanding of the interplay between renewable energy and power quality, the findings aim to facilitate the establishment of a sustainable and resilient energy system in Jordan. Beyond mitigating climate change and enhancing energy security, this research underscores the pivotal role of renewable energy in ushering in a greener, cleaner future for generations to come. 46 53 2024/12/012024/12/052024/12/052024/12/062024/12/06 1403/9/16 2025/02/182025/02/202025/02/232025/02/232025/02/25 1403/12/7 Malik Khalid Malik Khalid Faculty of Eectrical Engineering & Technology, University Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia. Malaysia almaleeh1987@yahoo.com Baharuddin Ismail Baharuddin Ismail Faculty of Eectrical Engineering & Technology, University Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia. Malaysia baha@unimap.edu.my Chanuri Charin Chanuri Charin Faculty of Eectrical Engineering & Technology, University Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia. Malaysia Arnawan Hasibuan Arnawan Hasibuan Teknik Elektro, Universitas Malikussaleh, Lhokseumawe, Aceh, Indonesia Indonesia Abd Alazeez Almaleeh Abd Alazeez Almaleeh Faculty of Eectrical Engineering & Technology, University Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia. Malaysia Wind Power Power Quality Total Harmonic Distortion Grid Integration [1] I. Citaristi, “International energy agency—iea,” in The Europa directory of international organizations 2022, Routledge, 2022, pp. 701–702.##[2] S. Tabassum et al., “Solar energy in the United States: Development, challenges and future prospects,” Energies, vol. 14, no. 23, p. 8142, 2021.##[3] R. E. S. IRENA, “International renewable energy agency,” Abu Dhabi, vol. 2020, 2020.##[4] G. W. E. Council, “Global offshore wind report 2020,” GWEC Brussels, Belgium, vol. 19, pp. 10–12, 2020.##[5] O. P. Mahela, B. Khan, H. Haes Alhelou, and S. Tanwar, “Assessment of power quality in the utility grid integrated with wind energy generation,” IET Power Electron., vol. 13, no. 13, pp. 2917–2925, 2020.##[6] H. Eroğlu, E. Cuce, P. M. Cuce, F. Gul, and A. Iskenderoğlu, “Harmonic problems in renewable and sustainable energy systems: A comprehensive review,” Sustain. Energy Technol. Assessments, vol. 48, p. 101566, 2021.##[7] A. Taghvaie, T. Warnakulasuriya, D. Kumar, F. Zare, R. Sharma, and D. M. Vilathgamuwa, “A Comprehensive Review of Harmonic Issues and Estimation Techniques in Power System Networks Based on Traditional and Artificial Intelligence/Machine Learning,” Ieee Access, vol. 11, pp. 31417–31442, 2023.##[8] L. Tang, Y. Han, P. Yang, C. Wang, and A. S. Zalhaf, “A review of voltage sag control measures and equipment in power systems,” Energy Reports, vol. 8, pp. 207–216, 2022.##[9] L. Ma, Y. Li, D. Tian, J. Lou, Y. Chen, and X. Liu, “Assessment of voltage sag/swell in the distribution network based on energy index and influence degree function,” Electr. Power Syst. Res., vol. 216, p. 109072, 2023.##[10] G. Abu-Rumman, A. I. Khdair, and S. I. Khdair, “Current status and future investment potential in renewable energy in Jordan: An overview,” Heliyon, vol. 6, no. 2, 2020.##[11] A. Badnjevic, “Evidence-based maintenance of medical devices: Current shortage and pathway towards solution,” Technol. Heal. Care, vol. 31, no. 1, pp. 293–305, 2023.##[12] G. S. Chawda et al., “Comprehensive review on detection and classification of power quality disturbances in utility grid with renewable energy penetration,” IEEE Access, vol. 8, pp. 146807–146830, 2020.##[13] Y. Xing, J. Liu, F. Li, G. Zhang, and J. Li, “Advanced dual-probes non-contact voltage measurement approach for AC/DC power transmission wire based on the electric field radiation principle,” IEEE Trans. Instrum. Meas., 2023.##[14] V. Kumar, A. S. Pandey, and S. K. Sinha, “Grid integration and power quality issues of wind and solar energy system: A review,” in 2016 International conference on emerging trends in electrical electronics & sustainable energy systems (ICETEESES), 2016, pp. 71–80.##[15] S. W. Ali et al., “Offshore Wind Farm-Grid Integration: A Review on Infrastructure, Challenges, and Grid Solutions,” IEEE Access, vol. 9. Institute of Electrical and Electronics Engineers Inc., pp. 102811–102827, 2021, doi: 10.1109/ACCESS.2021.3098705.##[16] M. Information, W. J. Olechiw, D. D. S. Senior, C. Dimitriu, F. D. Senior, and G. Mcleod, “Power Quality Monitoring Systems,” pp. 512–516, 2014.##[17] Y. Wang, R. Zou, F. Liu, L. Zhang, and Q. Liu, “A review of wind speed and wind power forecasting with deep neural networks,” Appl. Energy, vol. 304, p. 117766, 2021.##[18] L. Al-Ghussain et al., “100% renewable energy grid for rural electrification of remote areas: A case study in Jordan,” Energies, vol. 13, no. 18, p. 4908, 2020.##[19] N. Jargalsaikhan, H. Masrur, A. Iqbal, S. S. Rangarajan, S. Byambaa, and T. Senjyu, “A control algorithm to increase the efficient operation of wind energy conversion systems under extreme wind conditions,” Energy Reports, vol. 8, pp. 11429–11439, 2022.##[20] J. C. L. da Silva, T. Ramos, and M. F. Medeiros Júnior, “Modeling and harmonic impact mitigation of grid-connected scig driven by an electromagnetic frequency regulator,” Energies, vol. 14, no. 15, 2021, doi: 10.3390/en14154524.##[21] Y. Shuqair and F. Jazar, “Improving Grid Reliability in Jordan: A Proposal for Integrated Demand Side Management and Energy Storage Solutions,” Available SSRN 4533750.##[22] H. F. Sindi, S. Alghamdi, M. Rawa, A. I. Omar, and A. H. Elmetwaly, “Robust control of adaptive power quality compensator in Multi-Microgrids for power quality enhancement using puzzle optimization algorithm,” Ain Shams Eng. J., vol. 14, no. 8, p. 102047, 2023.##[23] P. Gayatri, G. D. Sukumar, and J. Jithendranath, “Effect of load change on source parameters in power system,” in 2015 Conference on Power, Control, Communication and Computational Technologies for Sustainable Growth (PCCCTSG), 2015, pp. 178–182.##[24] M. K. Alkasasbeh and E. K. Almaita, “Impact Study of Wind Generation on power quality of Electrical Power Grid (Jordan Wind Farm Case Study),” IOSR J. Electr. Electron. Eng., vol. 12, no. 03, pp. 99–108, 2017, doi: 10.9790/1676-12030299108.##[25] R. C. Dugan, Electrical power system quality. The McGraw Hill Companies, 2000.## ##

Double Sigmoid Activation Function for Fault Detection in Wind Turbine Generator using Artificial Neural Network 2 2 The activation function has gained popularity in the research community since it is the most crucial component of the artificial neural network (ANN) algorithm. However, the existing activation function is unable to accurately capture the value of several parameters that are affected by the fault, especially in wind turbines (WT). Therefore, a new activation function is suggested in this paper, which is called the double sigmoid activation function to capture the value of certain parameters that are affected by the fault. The fault detection in WT with a doubly fed induction generator (DFIG) is the basis for the ANN algorithm model that is presented in this study. The ANN model was developed in different activation functions, namely linear and double sigmoid activation functions to evaluate the effectiveness of the proposed activation function. The findings indicate that the model with a double sigmoid activation function has greater accuracy than the model with a linear activation function. Moreover, the double sigmoid activation function provides an accuracy of more than 82% in the ANN algorithm. In conclusion, the simulated response demonstrates that the proposed double sigmoid activation function in the ANN model can effectively be applied in fault detection for DFIG based WT model.  54 64 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/07 1403/9/17 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/18 1403/11/30 NOOR FAZLIANA FADZAIL NOOR FAZLIANA FADZAIL Faculty of Electrical Engineering & Technology, University Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia. Malaysia fazlianafadzail@unimap.edu.my Samila Mat Zali Samila Mat Zali Faculty of Electrical Engineering & Technology, University Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia. Malaysia samila@unimap.edu.my Ernie Che Mid Ernie Che Mid Faculty of Electrical Engineering & Technology, University Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia. Malaysia ernie@unimap.edu.my Activation Function Fault Detection Artificial Neural Network Machine Learning Doubly Fed Induction Generator Wind Turbine. [1] Y. Wang, Y. Li, Y. Song, and X. Rong, "The influence of the activation function in a convolution neural network model of facial expression recognition," Applied Sciences, vol. 10, no. 5, p. 1897, 2020, doi: 10.3390/app10051897.##[2] H. Jun, L. Shuai, S. Jinming, L. Yue, W. Jingwei and J. Peng, "Facial Expression Recognition Based on VGGNet Convolutional Neural Network," 2018 Chinese Automation Congress (CAC), Xi'an, China, 2018, pp. 4146-4151, doi: 10.1109/CAC.2018.8623238.##[3] Z. Huang, X. Du, L. Chen, Y. Li, M. Liu, Y. Chou, and L. 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Samatin Njikam and H. Zhao, "A novel activation function for multilayer feed-forward neural networks," Applied Intelligence, vol. 45, pp. 75–82, 2016, doi: 10.1007/s10489-015-0744-0.##[9] S. Sarkar, S. Agrawal, T. Baker, P. K. R. Maddikunta, and T. R. Gadekallu, "Catalysis of neural activation functions: Adaptive feed-forward training for big data applications," Applied Intelligence, vol. 52, no. 12, pp. 13364–13383, 2022, doi: 10.1007/s10489-021-03082-y.##[10] M. Varshney and P. Singh, "Optimizing nonlinear activation function for convolutional neural networks," Signal, Image and Video Processing, vol. 15, no. 6, pp. 1323–1330, 2021, doi: 10.1007/s11760-021-01863-z.##[11] M. Agarwal, S. Gupta, and K. K. Biswas, "A new Conv2D model with modified ReLU activation function for identification of disease type and severity in cucumber plant," Sustainable Computing: Informatics and Systems, vol. 30, p. 100473, 2021, doi: 10.1016/j.suscom.2020.100473.##[12] K. D. Anadkat and H. M. Diwanji, "Effect of activation function in speech emotion recognition on the RAVDESS dataset," Reliability: Theory & Applications, vol. 16, no. 3 (63), pp. 228–236, 2021.##[13] Q. Gao, X. Wu, J. Guo, H. Zhou, and W. Ruan, "Machine-learning-based intelligent mechanical fault detection and diagnosis of wind turbines," Mathematical Problems in Engineering, vol. 2021, no. 1, p. 9915084, 2021, doi: 10.1155/2021/9915084.##[14] S. Heo and J. H. Lee, "Fault detection and classification using artificial neural networks," IFAC-PapersOnLine, vol. 51, no. 18, pp. 470–475, 2018, doi: 10.1016/j.ifacol.2018.09.380.##[15] N. A. M. Leh, F. M. Zain, Z. Muhammad, S. A. Hamid and A. D. Rosli, "Fault Detection Method Using ANN for Power Transmission Line," 2020 10th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, 2020, pp. 79-84, doi: 10.1109/ICCSCE50387.2020.9204921##[16] E. B. M. 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Enhanced Lightweight YOLO Model for Efficient Vehicle Detection in Satellite Imagery 2 2 Vehicle detection in satellite images is a challenging task due to the variability in scale and resolution, complex background, and variability in object appearance. One-stage detection models are currently state-of-the-art in object detection due to their faster detection times. However, these models have complex architectures that require powerful processing units to train while generating a large number of parameters and achieving slow detection speed on embedded devices. To solve these problems, this work proposes an enhanced lightweight object detection model based on the YOLOv4 Tiny model. The proposed model incorporates multiple modifications, including integrating a Mix-efficient layer aggregation network within its backbone network to optimize efficiency by reducing parameter generation. Additionally, an improved small efficient layer aggregation network is adopted in the modified path aggregation network to enhance feature extraction across various scales. Finally, the proposed model incorporates the Swish function and an extra YOLO head for detection. The experimental results evaluated on the VEDAI dataset demonstrated that the proposed model achieved a higher mean average precision value and generated the smallest model size compared to the other lightweight models. Moreover, the proposed model achieved real-time performance on the NVIDIA Jetson Nano. These findings demonstrate that the proposed model offers the best trade-offs in terms of detection accuracy, model size, and detection time, making it highly suitable for deployment on embedded devices with limited capacity. 65 77 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/08 1403/9/18 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/20 1403/12/2 Mohamad Haniff Junos Mohamad Haniff Junos School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia. Malaysia haniffjunos@usm.my Anis Salwa Mohd Khairuddin Anis Salwa Mohd Khairuddin Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Kuala Lumpur, Malaysia. Malaysia anissalwa@um.edu.my Elmi Abu Bakar Elmi Abu Bakar School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia. Malaysia meelmi@usm.my Ahmad Faizul Hawary Ahmad Faizul Hawary School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia. Malaysia aefaizul@usm.my Lightweight architecture Modified YOLO Satellite Image Vehicle Detection. [1] H. V. Koay, J. H. Chuah, C. O. Chow, Y. L. Chang, and K. K. Yong, “YOLO-RTUAV: Towards real-time vehicle detection through aerial images with low-cost edge devices,” Remote Sens., vol. 13, no. 21, pp. 1–26, 2021, doi: 10.3390/rs13214196.##[2] S. Razakarivony and F. Jurie, “Vehicle detection in aerial imagery: A small target detection benchmark,” J. Vis. Commun. Image Represent., vol. 34, pp. 187–203, 2016, doi: 10.1016/j.jvcir.2015.11.002.##[3] A. Froidevaux et al., “Vehicle Detection and Counting from VHR Satellite Images: Efforts and Open Issues,” in IEEE nternational Geoscience and Remote Sensing Symposium, 2020, pp. 256–259, doi: 10.1109/IGARSS39084.2020.9323827.##[4] L. Pulvirenti, L. Rolando, and F. Millo, “Energy management system optimization based on an LSTM deep learning model using vehicle speed prediction,” Transp. Eng., vol. 11, no. January, p. 100160, 2023, doi: 10.1016/j.treng.2023.100160.##[5] M. Abbasi, A. Shahraki, and A. Taherkordi, “Deep learning for network traffic monitoring and analysis (NTMA): A survey,” Comput. Commun., vol. 170, no. January, pp. 19–41, 2021, doi: 10.1016/j.comcom.2021.01.021.##[6] Z. Yang and L. S. C. Pun-Cheng, “Vehicle detection in intelligent transportation systems and its applications under varying environments: A review,” Image Vis. Comput., vol. 69, pp. 143–154, 2018, doi: 10.1016/j.imavis.2017.09.008.##[7] L. Liu et al., “Deep Learning for Generic Object Detection: A Survey,” Int. J. Comput. Vis., vol. 128, no. 2, pp. 261–318, 2020, doi: 10.1007/s11263-019-01247-4.##[8] L. Jiao et al., “A survey of deep learning-based object detection,” IEEE Access, vol. 7, no. 3, pp. 128837–128868, 2019, doi: 10.1109/ACCESS.2019.2939201.##[9] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once : Unified, real-time object detection,” in IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 779–788, doi: 10.1109/CVPR.2016.91.##[10] J. Zhang, J. Lei, W. Xie, Z. Fang, Y. Li, and Q. Du, “SuperYOLO: Super resolution assisted object detection in multimodal remote sensing imagery,” IEEE Trans. Geosci. Remote Sens., vol. 61, pp. 1–14, 2023, doi: 10.1109/TGRS.2023.3258666.##[11] M. Sharma et al., “YOLOrs: Object detection in multimodal remote sensing imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 1497–1508, 2021, doi: 10.1109/JSTARS.2020.3041316.##[12] Q. Xu, Y. Li, and Z. Shi, “LMO-YOLO: A Ship Detection Model for Low-Resolution Optical Satellite Imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 15, pp. 4117–4131, 2022, doi: 10.1109/JSTARS.2022.3176141.##[13] M. T. Pham, L. Courtrai, C. Friguet, S. Lefèvre, and A. Baussard, “YOLO-fine: One-stage detector of small objects under various backgrounds in remote sensing images,” Remote Sens., vol. 12, no. 15, pp. 1–26, 2020, doi: 10.3390/RS12152501.##[14] M. F. Humayun, F. A. Nasir, F. A. Bhatti, M. Tahir, and K. Khurshid, “YOLO-OSD: Optimized Ship Detection and Localization in Multiresolution SAR Satellite Images Using a Hybrid Data-Model Centric Approach,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 17, pp. 5345–5363, 2024, doi: 10.1109/JSTARS.2024.3365807.##[15] A. Momin, M. Haniff, J. Anis, and S. Mohd, “Lightweight CNN model : automated vehicle detection in aerial images,” Signal, Image Video Process., 2022, doi: 10.1007/s11760-022-02328-7.##[16] A. Betti and M. Tucci, “YOLO-S : A lightweight and accurate YOLO-like network for small target detection in aerial imagery,” Sensors, vol. 23, p. 1865, 2023.##[17] Y. Yang, Z. Miao, H. Zhang, B. Wang, and L. Wu, “Lightweight Attention-Guided YOLO With Level Set Layer for Landslide Detection From Optical Satellite Images,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 17, pp. 3543–3559, 2024, doi: 10.1109/JSTARS.2024.3351277.##[18] C.-Y. Wang, H.-Y. M. Liao, and I.-H. Yeh, “Designing network design strategies through gradient path analysis,” 2022, doi: 10.6688/JISE.202307_39(4).0016.##[19] M. Tan and Q. V. Le, “MixConv: Mixed depthwise convolutional kernels,” 2019.##[20] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path Aggregation Network for Instance Segmentation,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018, pp. 8759–8768, doi: 10.1109/CVPR.2018.00913.##[21] A. N. Amudhan and A. P. Sudheer, “Lightweight and computationally faster Hypermetropic Convolutional Neural Network for small size object detection,” Image Vis. Comput., vol. 119, p. 104396, 2022, doi: 10.1016/j.imavis.2022.104396.## ##

A Comparative Study on DG Placement Using Marine Predator and Osprey Algorithms to Enhance Loss Reduction Index in the Distribution System 2 2 The Marine Predator Algorithm (MPA) and Osprey Optimization Algorithm (OOA) are nature-inspired metaheuristic techniques used for optimizing the location and sizing of distributed generation (DG) in power distribution systems. MPA simulates marine predators' foraging strategies through Lévy and Brownian movements, while OOA models the hunting and survival tactics of ospreys, known for their remarkable fishing skills. Effective placement and sizing of DG units are crucial for minimizing network losses and ensuring cost efficiency. Improper configurations can lead to overcompensation or undercompensation in the network, increasing operational costs. Different DG technologies, such as photovoltaic (PV), wind, microturbines, and generators, vary significantly in cost and performance, highlighting the importance of selecting the right models and designs. This study compares MPA and OOA in optimizing the placement of multiple DGs with two types of power injection which are active and reactive power. Simulations on the IEEE 69-bus reliability test system, conducted using MATLAB, demonstrated MPA’s superiority, achieving a 69% reduction in active power losses compared to OOA’s 61%, highlighting its potential for more efficient DG placement in power distribution systems. The proposed approach incorporates a DG model encompassing multiple technologies to ensure economic feasibility and improve overall system performance. 78 87 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/15 1403/9/25 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/20 1403/12/2 Syazwan Ahmad Sabri Syazwan Ahmad Sabri Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia Malaysia syazwan@studentmail.unimap.edu.my Siti Rafidah Abdul Rahim Siti Rafidah Abdul Rahim Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia Malaysia rafidah@unimap.edu.my Azralmukmin Azmi Azralmukmin Azmi Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia Malaysia azralmukmin@unimap.edu.my Syahrul Ashikin Azmi Syahrul Ashikin Azmi Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia Malaysia ashikin@unimap.edu.my Muhamad Hatta Hussain Muhamad Hatta Hussain Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia Malaysia muhdhatta@unimap.edu.my Ismail Musirin Ismail Musirin School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Selangor, Malaysia Malaysia ismailbm@uitm.edu.my Distributed Generation Nature Inspired Algorithm Marine Predator Algorithm Osprey Optimization Algorithm MATLAB Power Distribution IEEE 69-Bus Loss Reduction Index. [1] Nassef, Ahmed M., Mohammad Ali Abdelkareem, Hussein M. Maghrabie, and Ahmad Baroutaji. 2023. "Review of Metaheuristic Optimization Algorithms for Power Systems Problems" Sustainability 15, no. 12: 9434. https://doi.org/10.3390/su15129434##[2] Weiwei, Li., Hai, Yu., Tianchen, Gu., Lidong, Guo., Yi, Peng. (2024). Research on optimization configuration of distributed generation (DG) based on power supply reliability. https://doi.org/10.1117/12.3024460##[3] Hemant, Patel., Anurag, Kumar., Aashish, Kumar, Bohre., Omkar, Yadav. (2023). Reliability Based Performance Analysis of Distribution Network with Dispersed Generation Using Optimization. https://doi.org/10.1109/epee59859.2023.10351830##[4] Ustun, Taha Selim. 2023. "Microgrids Imitate Nature for Improved Performance—Use of Nature-Inspired Optimization Techniques in Future Power Systems" Energies 16, no. 3: 1522. https://doi.org/10.3390/en16031522##[5] Wu, Wei & Guo, Nian & Deng, Zhimin & Yu, Hui & Hu, Guangjing & Zhu, Bao & Zhao, Huijuan. (2019). Analysis of influence of distributed power supply on distribution network voltage considering permeability. IOP Conference Series: Earth and Environmental Science. 227. 032039. https://doi.org/10.1088/1755-1315/227/3/032039##[6] Zhendong, Wang & Xiao, Hang & Yang, Shuxin & Wang, Junling & Mahmoodi, Soroosh. (2022). Multi-Strategy Integrated Marine Predator Algorithm Applied to 3D Surface WSN Coverage Optimization. Wireless Communications and Mobile Computing. 2022. 10.1155/2022/9593103. https://doi.org/10.1155/2022/9593103##[7] Chalabi, Nour & Attia, Abdelouahab & Bouziane, Abderraouf & Hassaballah, M. (2023). An improved marine predator algorithm based on epsilon dominance and Pareto archive for multi-objective optimization. Engineering Applications of Artificial Intelligence. 119. 105718. https://doi.org/10.1016/j.engappai.2022.105718##[8] Belbachir, Nasreddine, Mohamed Zellagui, Samir Settoul, Claude Ziad El-Bayeh, and Ragab A. El- Sehiemy. 2023. "Multi Dimension-Based Optimal Allocation of Uncertain Renewable Distributed Generation Outputs with Seasonal Source-Load Power Uncertainties in Electrical Distribution Network Using Marine Predator Algorithm" Energies 16, no. 4: 1595. https://doi.org/10.3390/en16041595##[9] Mohammad Husni, Noor Najwa Husnaini & Abdul Rahim, Siti Rafidah & Adzman, Mohd Rafi & Hussain, M.H. & Musirin, Professor Dr. Ismail. (2021). Cost of Energy Losses for Distributed Generation Using Hybrid Evolutionary Programming Firefly Algorithm. Journal of Physics: Conference Series. 2107. 012049. https://doi.org/10.1088/1742-6596/2107/1/012049##[10] A. Selim, S. Kamel, A. S. Alghamdi and F. Jurado, "Optimal Placement of DGs in Distribution System Using an Improved Harris Hawks Optimizer Based on Single- and Multi-Objective Approaches," in IEEE Access, vol. 8, pp. 52815-52829, 2020, https://doi.org/10.1109/ACCESS.2020.2980245##[11] D. Xiaoqun, W. Jiahong and Z. Feng, "Optimal location and capacity of distributed generation based on scenario probability," 2009 International Conference on Sustainable Power Generation and Supply, Nanjing, China, 2009, pp. 1-5, https://doi.org/10.1109/SUPERGEN.2009.5348224##[12] Ramezani, M., Bahmanyar, D. & Razmjooy, N. A New Improved Model of Marine Predator Algorithm for Optimization Problems. Arab J Sci Eng 46, 8803-8826 (2021). https://doi.org/10.1007/s13369-021-05688-3##[13] Eid, Ahmad & Kamel, Salah & Abualigah, Laith. (2021). Marine predators algorithm for optimal allocation of active and reactive power resources in distribution networks. Neural Computing and Applications. https://doi.org/10.1007/s00521-021-06078-4##[14] Zhang, Yi, and Pengtao Liu. 2023. "Research on Reactive Power Optimization Based on Hybrid Osprey Optimization Algorithm" Energies 16, no. 20: 7101. https://doi.org/10.3390/en16207101##[15] Dehghani, Mohammad & Trojovsky, Pavel. (2023). Osprey optimization algorithm: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems. Frontiers in Mechanical Engineering. 8. 1126450. https://doi.org/10.3389/fmech.2022.1126450##[16] P. Marksan et al., "Marine Predators Algorithm Optimization for Economic Load Dispatch with Valve-Point Loading Effects," 2023 20th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Nakhon Phanom, Thailand, 2023, pp. 1-4, https://doi.org/10.1109/ECTI CON58255.2023.10153269##[17] A. M. Shaheen, R. A. El-Sehiemy, S. Kamel, E. E. Elattar and A. M. Elsayed, "Improving Distribution Networks' Consistency by Optimal Distribution System Reconfiguration and Distributed Generations," in IEEE Access, vol. 9, pp. 67186-67200, 2021, https://doi.org/10.1109/ACCESS.2021.3076670##[18] A. Saadat, R. -A. Hooshmand, A. Kiyoumarsi and M. Tadayon, "Optimal Location of Voltage Sag Monitors in Distribution Networks With DGs Using Network Zoning," in IEEE Transactions on Power Delivery, vol. 38, no. 6, pp. 4157-4165, Dec. 2023, https://doi.org/10.1109/TPWRD.2023.3302568##[19] H. Pachauri, A. Uniyal and S. Sarangi, "Optimal Location and Sizing of Multiple DGs to Improve Resiliency of Power System after an HILF event," 2021 9th IEEE International Conference on Power Systems (ICPS), Kharagpur, India, 2021, pp. 1-6, https://doi.org/10.1109/ICPS52420.2021.9670218##[20] Afshin Faramarzi, Mohammad Heidarinejad, Seyedali Mirjalili, Amir H. Gandomi, Marine Predators Algorithm: A nature-inspired metaheuristic, Expert Systems with Applications, Volume 152, 2020,113377, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2020.113377## ##

Online-Tuning Fuzzy Logic Controller Based Particle Swarm Optimization Proportional-Integral (FLC-PSO-PI) For Multilevel Inverter of Output Voltage Regulation 2 2 This paper focuses on the application of a cascaded multilevel inverter, specifically the 5-level multilevel inverter, utilizing a proposed controller known as the FLC-PSO-PI controller. The primary challenge addressed in this research is the precise regulation of output voltage in the multilevel inverter during load variations while meeting voltage harmonic and transition requirements as per industry standards, which are the 10 % voltage limit recommended by IEC and 8 % of total harmonic distortion (THD) by IEEE. An innovative solution is proposed by integrating PSO and FLC to dynamically adapt the controller in real-time, ensuring stable and accurate output voltage regulation. The proposed controller is designed and simulated using MATLAB/Simulink, and its performance is compared with PSO-PI and no controller under various load conditions. The results demonstrate that the FLC-PSO-PI controller significantly enhances output voltage regulation were achieving the desired peak voltage and low THD across different load scenarios, including half load to full load (0.8 %) and no load to full load (0.89 %). Furthermore, the FLC-PSO-PI controller exhibits superior transient response characteristics, such as reduced overshooting (2.89 %), faster rise time at 36.946 µs, and satisfactory settling time at 151.014 µs. This research contributes to the advancement of multilevel inverter technology and its potential applications in renewable energy systems, motor drives, and grid-connected devices. The proposed FLC-PSO-PI controller offers a promising solution for precise voltage regulation in multilevel inverters, enhancing their performance and enabling widespread adoption in various industrial sectors. 88 98 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/17 1403/9/27 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/22 1403/12/4 Ying Foo Leong Ying Foo Leong Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia. Malaysia yingfoo55@gmail.com Nizaruddin M. Nasir Nizaruddin M. Nasir Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia. Malaysia n99zrd@gmail.com Suliana Ab-Ghani Suliana Ab-Ghani Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia. Malaysia suliana@umpsa.edu.my Norazila Jaalam Norazila Jaalam Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia. Malaysia zila@umpsa.edu.my Nur Huda Ramlan Nur Huda Ramlan Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia. Malaysia hudaramlan@umpsa.edu.my Cascaded H-Bridge Multilevel Inverter (CHMI) Proportional-Integral (PI) Particle Swarm Optimization (PSO) Fuzzy Logic Controller (FLC) [1] A. Akbari, J. Ebrahimi, Y. Jafarian, and A. Bakhshai, ‘A multilevel inverter topology with an improved reliability and a reduced number of components’, IEEE J Emerg Sel Top Power Electron, vol. 10, no. 1, pp. 553–563, Feb. 2022, doi: 10.1109/JESTPE.2021.3089867.##[2] G. K. Srinivasan, M. Rivera, V. Loganathan, D. Ravikumar, and B. Mohan, ‘Trends and challenges in multi-level inverter with reduced switches’, Electronics 2021, Vol. 10, Page 368, vol. 10, no. 4, p. 368, Feb. 2021, doi: 10.3390/ELECTRONICS10040368.##[3] A. Lewicki, C. Odeh, and M. Morawiec, ‘Space vector pulsewidth modulation strategy for multilevel cascaded h-bridge inverter with dc-link voltage balancing ability’, IEEE Transactions on Industrial Electronics, vol. 70, no. 2, pp. 1161–1170, Feb. 2023, doi: 10.1109/TIE.2022.3158005.##[4] S. Hayat, S. S. Syed, S. S. Syed, S. Shabbir, and J. Khan, ‘63-level reduce switch asymmetrical cascaded h-bridge multilevel inverter’, 2020 IEEE 23rd International Multitopic Conference (INMIC), Nov. 2020, doi: 10.1109/INMIC50486.2020.9318100.##[5] S. K. Chien, S. Y. Sim, W. M. Utomo, S. L. Kek F. Mustafa, N. A. Zambri, A. J. L M. Siang and G. Y. Sim, ‘Enhanced DTC induction motor drives for THD minimization performance improvement with multilevel inverter’, International Journal of Power Electronics and Drive Systems (IJPEDS), vol. 13, no. 1, pp. 93–101, Mar. 2022, doi: 10.11591/IJPEDS.V13.I1.PP93-101.##[6] B. Mahato, S. Majumdar, K. C. Jana, A. Agrawal, and A. Shrivastava, ‘A generalized series-connected multilevel inverter (MLI) based on reduced power electronic devices for symmetrical/asymmetrical sources’, Arab J Sci Eng, vol. 48, no. 5, pp. 5907–5924, May 2023, doi: 10.1007/s13369-022-07066-z.##[7] R. Gadal, A. Oukennou, F. El Mariami, A. Belfqih, and N. Agouzoul, ‘Voltage stability assessment and control using indices and FACTS: A comparative review’, Journal of Electrical and Computer Engineering, vol. 2023, no. 1, p. 5419372, Jan. 2023, doi: 10.1155/2023/5419372.##[8] H. S. Salama and I. Vokony, ‘Voltage stability indices–A comparison and a review’, Computers & Electrical Engineering, vol. 98, p. 107743, Mar. 2022, doi: 10.1016/J.COMPELECENG.2022.107743.##[9] S. K. Sadula, R. Challoo, X. Fu, and S. Li, ‘Novel cost-effective technique for continued operation of electrical equipment during voltage sag’, International Journal of Engineering (IJE), vol. 13, no. 1, pp. 1–36, 2021.##[10] IEEE, ‘IEEE standard for harmonic control in electric power systems’, ANSI/IEEE Std. 519, vol. 2022, p. 17, Aug. 2022, doi: 10.1109/IEEESTD.2022.9848440.##[11] IEC, ‘IEC standard voltages’, IEC 60038:2009+AMD1:2021 CSV Consolidated version, 2021.##[12] H. Katir, A. Abouloifa, E. Elbouchikhi, A. Fekih, K. Noussi, and A. El Aroudi, ‘Robust control of cascaded h-bridge multilevel inverters for grid-tied PV systems subject to faulty conditions’, IEEE Control Syst Lett, vol. 7, pp. 2683–2688, 2023, doi: 10.1109/LCSYS.2023.3288494.##[13] J. H. Urrea-Quintero, J. N. Fuhg, M. Marino, and A. Fau, ‘PI/PID controller stabilizing sets of uncertain nonlinear systems: an efficient surrogate model-based approach’, Nonlinear Dyn, vol. 105, no. 1, pp. 277–299, Jul. 2021, doi: 10.1007/S11071-021-06431-1/FIGURES/15.##[14] S. Ab-Ghani, H. Daniyal, A. Z. Ahmad, N. Jaalam, N. M. Saad, N. H. Ramlan and N. Bahari, ‘Adaptive online auto-tuning using particle swarm optimized PI controller with time-variant approach for high accuracy and speed in Dual Active Bridge converter’, AIMS Electronics and Electrical Engineering, vol. 7, no. 2, pp. 156–170, 2023, doi: 10.3934/electreng.2023009.##[15] S. Ab-Ghani, H. Daniyal, N. Jaalam, N. M. Saad, and N. H. Ramlan, ‘Dynamic control and performance of dual active bridge converter based particle swarm optimization’, IET Conference Proceedings, vol. 2022, no. 22, pp. 312–316, 2022, doi: 10.1049/ICP.2022.2633.##[16] S. Ab-Ghani, H. Daniyal, N. H. Ramlan, and M. C. Tiong, ‘Online PSO-tuned phase shift angle controller for dual active bridge DC–DC converter’, SN Appl Sci, vol. 2, no. 1, pp. 1–8, Jan. 2020, doi: 10.1007/S42452-019-1782-8/TABLES/4.##[17] M.Izzat Nordin, Leong Ying Foo, M.H.A. Ab Malek, Suliana Ab Ghani, and N.Huda Ramlan, ‘Performance analysis of reduced switch hybrid cascaded multilevel inverter with PSO-based PI controller during load variations’, Proceedings of International Exchange and Innovation Conference on Engineering & Sciences (IEICES), vol. 10, pp. 874–881, Oct. 2024, doi: 10.5109/7323363.##[18] A. Tamer, L. Zellouma, M. T. Benchouia, and A. Krama, ‘Adaptive linear neuron control of three-phase shunt active power filter with anti-windup PI controller optimized by particle swarm optimization’, Computers & Electrical Engineering, vol. 96, p. 107471, Dec. 2021, doi: 10.1016/J.COMPELECENG.2021.107471.##[19] C. Sain, A. Banerjee, P. K. Biswas, T. S. Babu, and T. Dragicevic, ‘Updated PSO optimised fuzzy-PI controlled buck type multi-phase inverter-based PMSM drive with an over-current protection scheme’, IET Electr Power Appl, vol. 14, no. 12, pp. 2331–2339, Dec. 2020, doi: 10.1049/IET-EPA.2020.0165.##[20] A. Bouchakour, A. Borni, and M. Brahami, ‘Comparative study of P&O-PI and fuzzy-PI MPPT controllers and their optimisation using GA and PSO for photovoltaic water pumping systems’, International Journal of Ambient Energy, vol. 42, no. 15, pp. 1746–1757, Nov. 2021, doi: 10.1080/01430750.2019.1614988.##[21] R. Kumar, ‘Fuzzy particle swarm optimization control algorithm implementation in photovoltaic integrated shunt active power filter for power quality improvement using hardware-in-the-loop’, Sustainable Energy Technologies and Assessments, vol. 50, p. 101820, Mar. 2022, doi: 10.1016/J.SETA.2021.101820.##[22] J. Siahbalaee and N. Sanaie, ‘Comparison of conventional and new cascaded multilevel inverter topologies based on novel indices’, ISA Trans, vol. 119, pp. 41–51, Jan. 2022, doi: 10.1016/J.ISATRA.2021.02.025.## ##

Hyperbolic Detection of Ground Penetrating Radar for Buried Pipes Utilities Using Viola Jones 2 2 GPR (Ground Penetrating Radar) is well-known as an effective non-invasive imaging approach for shallow nature underground discovery, like finding and locating submerged objects. Although GPR has achieved some success, it is difficult to automatically process GPR images because human experts must interpret GPR images of buried objects. This can happen due to the possibility of a variety of mediums or underground noises from the environment, especially rocks and roots of trees. Thus, detecting hyperbolic echo characteristics is critical. As a result, Viola Jones detection is used to determine whether the presence of a hyperbolic signature underground indicates a pipe or not. GPR can also be used in the public works department because it is a non-destructive tool. Workers, for example, should be aware of the pipe size that must be replaced when it leaks. The original GPR image already shows hyperbolic image distortion due to pipe refraction. The current method is unreliable due to its lack of flexibility. As a result, there is another method for resolving this issue. Thus, the image will be pre-processed to eliminate or reduce background noise in the GPR input image. The results of this project demonstrate that the Viola Jones algorithm can accurately detect hyperbolic patterns in GPR images. 99 106 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/18 1403/9/28 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/01 1403/12/11 Nurul Syahirah Mohd Ideris Nurul Syahirah Mohd Ideris Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia syahirahideris@studentmail.unimap.edu.my Hasimah Ali Hasimah Ali Centre of Excellence for Intelligent Robotics and Autonomous Systems (CIRAS), Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Malaysia hasimahali@unimap.edu.my Mohd Shuhanaz Zanar Azalan Mohd Shuhanaz Zanar Azalan Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Malaysia shuhanaz@unimap.edu.my Tengku Sarah Tengku Amran Tengku Sarah Tengku Amran Department of Agensi Nuklear Malaysia, Bangi Selangor, Malaysia Malaysia sarah@nm.gov.my ground penetrating radar (GPR) image pre-processing hyperbolic detection Viola Jones. [1] N. Smitha and V. Singh, “Target detection using supervised machine learning algorithms for GPR data,” Sens. Imaging, vol. 21, no. 1, pp. 1–15, 2020, doi: 10.1007/s11220-020-0273-8.##[2] D. Liu et al., “Enhancing Ground-Penetrating Radar (GPR) Data Resolution Through Weakly Supervised Learning,” IEEE Trans. Geosci. Remote Sens., vol. 62, pp. 1–13, 2024, doi: 10.1109/TGRS.2024.3410184.##[3] G. Zhou, B. Yuan, Y. Gu, X. Shang, and J. Qi, “A Design of a New Strong Electromagnetic Pulse Ground-penetrating Radar System,” Prog. Electromagn. Res. Symp., vol. 2022-April, pp. 472–477, 2022, doi: 10.1109/PIERS55526.2022.9792828.##[4] Z. Liu, X. Gu, J. Chen, D. Wang, Y. Chen, and L. Wang, “Automatic recognition of pavement cracks from combined GPR B-scan and C-scan images using multiscale feature fusion deep neural networks,” Autom. Constr., vol. 146, no. June 2022, p. 104698, 2023, doi: 10.1016/j.autcon.2022.104698.##[5] X. Li, S. Ye, Q. Kong, C. Song, X. Liu, and G. Fang, “A Real-Time Permittivity Estimation Method for Stepped-Frequency Ground-Penetrating Radar by Full-Waveform Inversion,” Remote Sens., vol. 15, no. 21, 2023, doi: 10.3390/rs15215188.##[6] X. L. Travassos, S. L. Avila, and N. Ida, “Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review,” Applied Computing and Informatics, vol. 17, no. 2. Emerald Group Holdings Ltd., pp. 296–308, Apr. 29, 2021, doi: 10.1016/j.aci.2018.10.001.##[7] S. J. Savita and A. Pallavi, “Modeling of GPR Using gprMax Simulation,” IEEE Int. Conf. Distrib. Comput. Electr. Circuits Electron. ICDCECE 2022, pp. 1–4, 2022, doi: 10.1109/ICDCECE53908.2022.9792883.##[8] H. M. Alshamy, J. W. A. Sadah, and T. R. Saeed, “Recognizing of the A-Scan Image of a Buried Object Using a Deep Network,” Ac, pp. 752–757, 2022, doi: 10.1109/AEST55805.2022.10413011.##[9] T. Xu, D. Yuan, P. Wang, G. Yang, B. Li, and W. Sun, “Improved 3-D Representation of GPR Pipelines B-Scan Sequences Using a Neural Network Framework,” IEEE Trans. Geosci. Remote Sens., vol. 62, pp. 1–16, 2024, doi: 10.1109/TGRS.2024.3360101.##[10] S. Li, X. Cui, L. Guo, L. Zhang, X. Chen, and X. Cao, “Enhanced Automatic Root Recognition and Localization in GPR Images Through a YOLOv4-Based Deep Learning Approach,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1–14, 2022, doi: 10.1109/TGRS.2022.3181202.##[11] I. Agafta, B. Setiawan, and S. Wibowo, “Navigation Guidance System of AGV Robot Using Image Processing With HSV Filter,” Proc. - IEIT 2023 2023 Int. Conf. Electr. Inf. Technol., pp. 88–94, 2023, doi: 10.1109/IEIT59852.2023.10335571.##[12] P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, vol. 1, doi: 10.1109/cvpr.2001.990517.##[13] Sumanto, B. Wijonarko, M. Qommarudin, A. Sudibyo, P. Widodo, and A. M. Lukman, “Viola-Jones Algorithm for Face Detection using Wider Face Dataset,” 2022 10th Int. Conf. Cyber IT Serv. Manag. CITSM 2022, pp. 1–4, 2022, doi: 10.1109/CITSM56380.2022.9935830.##[14] M. B. Savadatti, A. B. Gurulakshmi, N. Nataraj, R. Anusha, S. Yoshitha, and S. Yadav, “Theoretical Analysis of Viola-Jones Algorithm Based Image and Live-Feed Facial Recognition,” Proc. - IEEE Int. Conf. Adv. Comput. Commun. Appl. Informatics, ACCAI 2022, pp. 1–7, 2022, doi: 10.1109/ACCAI53970.2022.9752590.##[15] S. Tasfia and S. Reno, “Face Mask Detection Using Viola-Jones and Cascade Classifier,” Proc. - Int. Conf. Augment. Intell. Sustain. Syst. ICAISS 2022, pp. 563–569, 2022, doi: 10.1109/ICAISS55157.2022.10011114.##[16] M. Tian, X. Li, S. Kong, L. Wu, and J. Yu, “A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot,” Front. Inf. Technol. Electron. Eng., vol. 23, no. 8, pp. 1217–1228, 2022, doi: 10.1631/FITEE.2100473.##[17] Y. Wang, G. Cui, and J. Xu, “Semi-automatic detection of buried rebar in GPR data using a genetic algorithm,” Autom. Constr., vol. 114, no. 2017, p. 103186, Jun. 2020, doi: 10.1016/j.autcon.2020.103186.##[18] T. Shanmukhaprasanthi, S. M. Rayavarapu, Y. L. Lavanya, and G. S. Rao, “A Comprehensive Study of Image Inpainting Techniques with Algorithmic approach,” 2023 6th Int. Conf. Inf. Syst. Comput. Networks, ISCON 2023, no. c, pp. 1–5, 2023, doi: 10.1109/ISCON57294.2023.10112205.##[19] M. García-Fernández, G. Álvarez-Narciandi, J. Laviada, Y. Á. López, and F. Las-Heras, “Towards real-time processing for UAV-mounted GPR-SAR imaging systems,” ISPRS J. Photogramm. Remote Sens., vol. 212, pp. 1–12, 2024, doi: 10.1016/j.isprsjprs.2024.04.008.##[20] A. Zaki, Y. Jusman, M. A. Megat Johari, and W. M. Aminuddin Wan Hussin, “Experimental Assessment of the Concrete Slab with Different Depth and Diameter of Steel Rebar using GPR and Image Processing,” Proceeding - 1st Int. Conf. Inf. Technol. Adv. Mech. Electr. Eng. ICITAMEE 2020, pp. 249–254, 2020, doi: 10.1109/ICITAMEE50454.2020.9398322.##[21] A. Bahmeie and M. Shakiba, “Comparing MTCNN and Viola-Jones Algorithm in Face Recognition,” 2024 19th Iran. Conf. Intell. Syst., pp. 68–72, 2024, doi: 10.1109/ICIS64839.2024.10887519.##[22] S. Suryavanshi, A. Dubey, and P. Sharma, “Detection of Facial Components Utilizing a Modified Version of the Viola-Jones Algorithm,” 4th Int. Conf. Inven. Res. Comput. Appl. ICIRCA 2022 - Proc., no. Icirca, pp. 1765–1772, 2022, doi: 10.1109/ICIRCA54612.2022.9985518.## ##

Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods 2 2 Photovoltaic (PV) systems are vital in the global renewable energy landscape because of their capability to harness solar energy efficiently. Ensuring the continuous and efficient operation of PV systems is crucial in maximizing their energy contribution. However, these systems' reliability and safety remain critical because they are prone to various faults, mainly when operating in harsh environmental conditions. This study addresses these issues by exploring fault detection and classification in PV arrays using neural network (NN) -based techniques. A PV array model, consisting of 3x6 PV modules, was simulated using MATLAB Simulink to replicate real-world conditions and analyse various fault scenarios. An open circuit, a short circuit, and a degrading fault are the three types of faults considered in this study. The NN was trained on a dataset generated from the MATLAB Simulink model, encompassing normal operating and fault conditions. This training enables the network to learn the distinctive patterns associated with each fault type, enhancing its detection accuracy and classification capabilities. Simulation results demonstrate that the NN-based approach effectively identifies and classifies the three types of faults. 107 115 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/19 1403/9/29 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/22 1403/12/4 Arizadayana Zahalan Arizadayana Zahalan Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Malaysia Samila Mat Zali Samila Mat Zali Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Malaysia samila@unimap.edu.my Ernie Che Mid Ernie Che Mid Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Malaysia Noor Fazliana Fadzail Noor Fazliana Fadzail Faculty of Electrical Engineering and Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia Malaysia Photovoltaic Arrays Fault Detection Fault Classification Neural Network [1] Y. Y. Hong and R. A. Pula, “Methods of photovoltaic fault detection and classification: A review,” Energy Reports, vol. 8, pp. 5898–5929, 2022, doi: 10.1016/j.egyr.2022.04.043.##[2] A. Mellit, G. M. Tina, and S. A. Kalogirou, “Fault detection and diagnosis methods for photovoltaic systems: A review,” Renewable and Sustainable Energy Reviews, vol. 91. 2018, doi: 10.1016/j.rser.2018.03.062.##[3] S. R. Madeti and S. N. Singh, “A comprehensive study on different types of faults and detection techniques for solar photovoltaic system,” Sol. Energy, vol. 158, no. June, pp. 161–185, 2017, doi: 10.1016/j.solener.2017.08.069.##[4] T. Pei and X. Hao, “A fault detection method for photovoltaic systems based on voltage and current observation and evaluation,” Energies, vol. 12, no. 9, 2019, doi: 10.3390/en12091712.##[5] N. Rakesh, S. Banerjee, S. Subramaniam, and N. Babu, “A simplified method for fault detection and identification of mismatch modules and strings in a grid-tied solar photovoltaic system,” Int. J. Emerg. Electr. Power Syst., vol. 21, no. 4, 2020, doi: 10.1515/ijeeps-2020-0001.##[6] J. M. Huang, R. J. Wai, and W. Gao, “Newly-designed fault diagnostic method for solar photovoltaic generation system based on IV-Curve measurement,” IEEE Access, vol. 7, 2019, doi: 10.1109/ACCESS.2019.2919337.##[7] R. Lipták and I. Bodnár, “Simulation of fault detection in photovoltaic arrays,” Analecta Tech. Szeged., vol. 15, no. 2, pp. 31–40, 2021, doi: 10.14232/analecta.2021.2.31-40.##[8] A. Livera, M. Theristis, G. Makrides, and G. E. Georghiou, “Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems,” Renew. Energy, vol. 133, pp. 126–143, 2019, doi: 10.1016/j.renene.2018.09.101.##[9] W. Chine, A. Mellit, V. Lughi, A. Malek, G. Sulligoi, and A. Massi Pavan, “A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks,” Renew. Energy, vol. 90, pp. 501–512, 2016, doi: 10.1016/j.renene.2016.01.036.##[10] M. Hussain, M. Dhimish, S. Titarenko, and P. Mather, “Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters,” Renew. Energy, vol. 155, pp. 1272–1292, 2020, doi: 10.1016/j.renene.2020.04.023.##[11] E. Garoudja, A. Chouder, K. Kara, and S. Silvestre, “An enhanced machine learning based approach for failures detection and diagnosis of PV systems,” Energy Convers. Manag., vol. 151, no. June, pp. 496–513, 2017, doi: 10.1016/j.enconman.2017.09.019.##[12] S. A. Zaki, H. Zhu, M. Al Fakih, A. R. Sayed, and J. Yao, “Deep-learning–based method for faults classification of PV system,” IET Renew. Power Gener., vol. 15, no. 1, pp. 193–205, 2020, doi: 10.1049/rpg2.12016.##[13] A. Hichri et al., “Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems,” Sustain., vol. 14, no. 17, 2022, doi: 10.3390/su141710518.##[14] V. Veerasamy et al., “LSTM Recurrent Neural Network Classifier for High Impedance Fault Detection in Solar PV Integrated Power System,” IEEE Access, vol. 9, pp. 32672–32687, 2021, doi: 10.1109/ACCESS.2021.3060800.##[15] N. F. Nicaire, P. N. Steve, N. E. Salome, and A. O. Grégroire, “Parameter Estimation of the Photovoltaic System Using Bald Eagle Search (BES) Algorithm,” Int. J. Photoenergy, vol. 2021, 2021, doi: 10.1155/2021/4343203.##[16] M. Zebiri, M. Mediouni, and H. Idadoub, “Modeling and simulation of the shading effect on the performance of a photovoltaic module in the presence of the bypass diode.,” E3S Web Conf., vol. 37, 2018, doi: 10.1051/e3sconf/20183706002.##[17] C. Wei and L. L. H. Idris, “An Improved Oblique Asymptote Method for Parameter Identification of PV Panels,” 2018 IEEE 7th World Conf. Photovolt. Energy Conversion, WCPEC 2018 - A Jt. Conf. 45th IEEE PVSC, 28th PVSEC 34th EU PVSEC, vol. 9781538685, no. May, pp. 386–389, 2018, doi: 10.1109/PVSC.2018.8548156.##[18] N. Hamid, M. Elyaqouti, N. Boulfaf, M. Feddaoui, and D. Agliz, “Modelling and characterisation of photovoltaic modules using iterative and analytical methods,” Int. J. Ambient Energy, 2021, doi: 10.1080/01430750.2021.1997809.##[19] Z. Chen, L. Wu, S. Cheng, P. Lin, Y. Wu, and W. Lin, “Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics,” Appl. Energy, vol. 204, pp. 912–931, 2017, doi: 10.1016/j.apenergy.2017.05.034.##[20] S. N. A. Mohd Ghazali and M. Z. Sujod, “A multi-scale dual-stage model for PV array fault detection, classification, and monitoring technique,” Int. J. Appl. Power Eng., vol. 11, no. 2, pp. 134–144, 2022, doi: 10.11591/ijape.v11.i2.pp134-144.##[21] P. J. Gnetchejo, S. N. Essiane, P. Ele, A. Dadjé, and Z. Chen, “Faults diagnosis in a photovoltaic system based on multivariate statistical analysis,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 00, no. 00, pp. 1–22, 2021, doi: 10.1080/15567036.2021.1919792.##[22] W. He, D. Yin, K. Zhang, X. Zhang, and J. Zheng, “Fault detection and diagnosis method of distributed photovoltaic array based on fine-tuning naive bayesian model,” Energies, vol. 14, no. 14, 2021, doi: 10.3390/en14144140.##[23] A. S. Orukotan, “Fault Detection and Classification of a Single Phase Inverter Using Artificial Neural Networks,” 2020, [Online]. Available: https://cornerstone.lib.mnsu.edu/etds.https://cornerstone.lib.mnsu.edu/etds/1043%0Ahttps://cornerstone.lib.mnsu.edu/etdshttps://cornerstone.lib.mnsu.edu/etds/428/.##[24] M. Klimo, P. Lukáč, and P. Tarábek, “Deep neural networks classification via binary error‐detecting output codes,” Appl. Sci., vol. 11, no. 8, 2021, doi: 10.3390/app11083563.## ##

Deep Learning for Identification Malaria Diseases from Microscopic Image 2 2 Malaria is a parasitic disease that causes significant morbidity and mortality worldwide. Early diagnosis and treatment are crucial for preventing complications and improving patient outcomes. Microscopic examination of blood smears remains the gold standard for malaria diagnosis, but it is time-consuming and requires skilled technicians. Deep learning has emerged as a promising tool for automated image analysis, including malaria diagnosis. In this study, we propose a novel approach for identifying malaria parasites in microscopic images using the GoogLeNet. Our method includes enhancement with the AGCS method, color transformation with grayscale, adaptive thresholding for segmentation, extraction, and GoogLeNet-based classification. We evaluated our method on a dataset of malaria blood smear images and achieved an accuracy of 95%, demonstrating the potential of GoogLeNet for automated malaria diagnosis. 116 123 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/23 1403/10/3 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/22 1403/12/4 Edy Victor Haryanto S Edy Victor Haryanto S Department of Engineering and Computer Science, Universitas Potensi Utama, Indonesia. Indonesia edy@potensi-utama.ac.id Aimi Salihah Abdul Nasir Aimi Salihah Abdul Nasir Department of Electrical Engineering and Technology, Universiti Malaysia Perlis, Malaysia. Malaysia aimisalihah@unimap.edu.my Mohd Yusoff Mashor Mohd Yusoff Mashor Department of Electrical Engineering and Technology, Universiti Malaysia Perlis, Malaysia. Malaysia yusoff@unimap.edu.my Bob Subhan Riza Bob Subhan Riza Department of Engineering and Computer Science, Universitas Potensi Utama, Indonesia. Indonesia bob@potensi-utama.ac.id Zeehaida Mohamed Zeehaida Mohamed Department of Medical Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Malaysia. Malaysia zeehaida@usm.edu.my Malaria diseases deep learning microscopic image identification [1] F. Hashmi, S. Aqeel, U. F. Zuberi, and W. Khan, “A systematic review and meta-analysis of inflammatory biomarkers associated with malaria infection and disease severity,” Cytokine, vol. 169, p. 156305, Sep. 2023, doi: 10.1016/J.CYTO.2023.156305.##[2] A. Requena-Méndez et al., “Malaria parasite prevalence among migrants: a systematic review and meta-analysis,” Clin. Microbiol. Infect., vol. 29, no. 12, pp. 1528–1537, Dec. 2023, doi: 10.1016/J.CMI.2023.09.010.##[3] S. H. Khan, N. S. Shah, R. Nuzhat, A. Majid, H. Alquhayz, and A. Khan, “Malaria parasite classification framework using a novel channel squeezed and boosted CNN,” Microscopy, vol. 71, no. 5, pp. 271–282, 2022, doi: 10.1093/jmicro/dfac027.##[4] V. Reddy, D. J. Weiss, J. Rozier, F. O. ter Kuile, and S. Dellicour, “Global estimates of the number of pregnancies at risk of malaria from 2007 to 2020: a demographic study,” Lancet Glob. Heal., vol. 11, no. 1, pp. e40–e47, Jan. 2023, doi: 10.1016/S2214-109X(22)00431-4.##[5] R. Liu et al., “AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images,” Patterns, vol. 4, no. 9, p. 100806, Sep. 2023, doi: 10.1016/J.PATTER.2023.100806.##[6] G. N. Uyaiabasi et al., “The question of the early diagnosis of asymptomatic and subpatent malaria in pregnancy: Implications for diagnostic tools in a malaria endemic area,” Eur. J. Obstet. Gynecol. Reprod. Biol. X, vol. 19, no. August, p. 100233, 2023, doi: 10.1016/j.eurox.2023.100233.##[7] S. Saxena, P. Sanyal, M. Bajpai, R. Prakash, and S. Kumar, “Trials and tribulations: Developing an artificial intelligence for screening malaria parasite from peripheral blood smears,” Med. J. Armed Forces India, Dec. 2023, doi: 10.1016/J.MJAFI.2023.10.007.##[8] S. L. Rei Yan, F. Wakasuqui, and C. Wrenger, “Point-of-care tests for malaria: speeding up the diagnostics at the bedside and challenges in malaria cases detection,” Diagn. Microbiol. Infect. Dis., vol. 98, no. 3, p. 115122, Nov. 2020, doi: 10.1016/J.DIAGMICROBIO.2020.115122.##[9] B. E. Fan et al., “From microscope to micropixels: A rapid review of artificial intelligence for the peripheral blood film,” Blood Rev., vol. 64, p. 101144, Mar. 2024, doi: 10.1016/J.BLRE.2023.101144.##[10] S. Aqeel, Z. Haider, and W. Khan, “Towards digital diagnosis of malaria: How far have we reached?,” J. Microbiol. Methods, vol. 204, p. 106630, Jan. 2023, doi: 10.1016/J.MIMET.2022.106630.##[11] G. Akafity, N. Kumi, and J. Ashong, “Diagnosis and management of malaria in the intensive care unit,” J. Intensive Med., vol. 4, no. 1, pp. 3–15, Jan. 2024, doi: 10.1016/J.JOINTM.2023.09.002.##[12] M. H. D. Alnussairi and A. A. İbrahim, “Malaria parasite detection using deep learning algorithms based on (CNNs) technique,” Comput. Electr. Eng., vol. 103, p. 108316, Oct. 2022, doi: 10.1016/J.COMPELECENG.2022.108316.##[13] D. T. Rademaker et al., “Quantifying the deformability of malaria-infected red blood cells using deep learning trained on synthetic cells,” iScience, vol. 26, no. 12, p. 108542, Dec. 2023, doi: 10.1016/J.ISCI.2023.108542.##[14] C. Ikerionwu et al., “Application of machine and deep learning algorithms in optical microscopic detection of Plasmodium: A malaria diagnostic tool for the future,” Photodiagnosis Photodyn. Ther., vol. 40, p. 103198, Dec. 2022, doi: 10.1016/J.PDPDT.2022.103198.##[15] A. M. Qadri, A. Raza, F. Eid, and L. Abualigah, “A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images,” Decis. Anal. J., vol. 9, p. 100352, Dec. 2023, doi: 10.1016/J.DAJOUR.2023.100352.##[16] A. Balaram, M. Silparaj, and R. Gajula, “Detection of malaria parasite in thick blood smears using deep learning,” Mater. Today Proc., vol. 64, pp. 511–516, Jan. 2022, doi: 10.1016/J.MATPR.2022.04.1012.##[17] M. Bhuiyan and M. S. Islam, “A new ensemble learning approach to detect malaria from microscopic red blood cell images,” Sensors Int., vol. 4, p. 100209, Jan. 2023, doi: 10.1016/J.SINTL.2022.100209.##[18] J. Somasekar and B. Eswara Reddy, “Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging,” Comput. Electr. Eng., vol. 45, pp. 336–351, Jul. 2015, doi: 10.1016/J.COMPELECENG.2015.04.009.##[19] M. Maity, A. Jaiswal, K. Gantait, J. Chatterjee, and A. Mukherjee, “Quantification of malaria parasitaemia using trainable semantic segmentation and capsnet,” Pattern Recognit. Lett., vol. 138, pp. 88–94, Oct. 2020, doi: 10.1016/J.PATREC.2020.07.002.##[20] A. de Souza Oliveira, M. Guimarães Fernandes Costa, M. das Graças Vale Barbosa, and C. Ferreira Fernandes Costa Filho, “A new approach for malaria diagnosis in thick blood smear images,” Biomed. Signal Process. Control, vol. 78, p. 103931, Sep. 2022, doi: 10.1016/J.BSPC.2022.103931.##[21] D. R. Loh, W. X. Yong, J. Yapeter, K. Subburaj, and R. Chandramohanadas, “A deep learning approach to the screening of malaria infection: Automated and rapid cell counting, object detection and instance segmentation using Mask R-CNN,” Comput. Med. Imaging Graph., vol. 88, p. 101845, Mar. 2021, doi: 10.1016/J.COMPMEDIMAG.2020.101845.##[22] B. Hemalatha, B. Karthik, C. V. Krishna Reddy, and A. Latha, “Deep learning approach for segmentation and classification of blood cells using enhanced CNN,” Meas. Sensors, vol. 24, p. 100582, Dec. 2022, doi: 10.1016/J.MEASEN.2022.100582.##[23] S. Chakraborty and K. Mali, “Fuzzy and elitist cuckoo search based microscopic image segmentation approach,” Appl. Soft Comput., vol. 130, p. 109671, Nov. 2022, doi: 10.1016/J.ASOC.2022.109671.##[24] S. Edy Victor Haryanto, M. Y. Mashor, A. S. A. Nasir, and Z. Mohamed, “Identification of Giemsa Staind of Malaria Using K-Means Clustering Segmentation Technique,” in 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018, 2019. doi: 10.1109/CITSM.2018.8674254.##[25] S. E. V. Haryanto, M. Y. Mashor, A. S. A. Nasir, and H. Jaafar, “A fast and accurate detection of Schizont plasmodium falciparum using channel color space segmentation method,” in 2017 5th International Conference on Cyber and IT Service Management, CITSM 2017, Institute of Electrical and Electronics Engineers Inc., Oct. 2017. doi: 10.1109/CITSM.2017.8089290.##[26] S. Dash et al., “Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction,” Symmetry (Basel)., vol. 14, no. 2, pp. 1–19, 2022, doi: 10.3390/sym14020194.##[27] T. A. Aris, A. S. A. Nasir, W. A. Mustafa, M. Y. Mashor, E. V. Haryanto, and Z. Mohamed, “Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images,” Diagnostics 2023, Vol. 13, Page 511, vol. 13, no. 3, p. 511, Jan. 2023, doi: 10.3390/DIAGNOSTICS13030511.##[28] H. A. Nugroho and R. Nurfauzi, “A combination of optimized threshold and deep learning-based approach to improve malaria detection and segmentation on PlasmoID dataset,” FACETS, vol. 8, no. 1, pp. 1–12, Aug. 2023, doi: 10.1139/FACETS-2022-0206.##[29] R. Rosnelly, B. S. Riza, L. Wahyuni, S. Suparni, A. Prasetio, and R. Rahim, “Improvement of Hybrid Image Enhancement for Detection and Classification of Malaria Disease Types and Stages with Artificial Intelligence,” TEM J., vol. 11, no. 2, pp. 535–542, 2022, doi: 10.18421/TEM112-06.##[30] W. M. Fong Amaris, C. Martinez, L. J. Cortés-Cortés, and D. R. Suárez, “Image features for quality analysis of thick blood smears employed in malaria diagnosis,” Malar. J., vol. 21, no. 1, pp. 1–12, 2022, doi: 10.1186/s12936-022-04064-2.##[31] Q. A. Arshad et al., “A dataset and benchmark for malaria life-cycle classification in thin blood smear images,” Neural Comput. Appl., vol. 34, no. 6, pp. 4473–4485, 2022, doi: 10.1007/s00521-021-06602-6.## ##

Harmonics Elimination of Reduced Switch Multilevel Inverter Using Henry Gas Solubility Optimization Algorithm 2 2 This study introduces a pioneering method to enhance the efficiency and effectiveness of three-phase five-level reduced switch cascaded H-bridge multilevel inverters (CHB MLI) by employing the Henry Gas Solubility Optimization (HGSO) algorithm. Targeting the selective harmonic elimination (SHE) technique, the research emphasizes the optimization of switching angles to significantly reduce total harmonic distortion (THD) and align the fundamental output voltage closely with the reference voltage. Central to this exploration are three distinct objective functions (OFs), meticulously designed to assess the HGSO algorithm’s performance across various modulation indices. Simulation results, facilitated by PSIM software, illustrate the impactful role these objective functions play in the optimization process. OF1 demonstrated a superior ability in generating low OF values and maintaining a consistent match between reference and fundamental voltages across the modulation index spectrum. Regarding the reduction of THD, it is crucial to emphasize that all OFs can identify the most effective switching angle to minimize THD and eliminate the fifth harmonic to a level below 0.1%. The findings highlight the potential of HGSO in solving complex optimization challenges within power electronics, offering a novel pathway for advancing modulation strategies in CHB MLIs and contributing to the development of more efficient, reliable, and compact power conversion systems. 124 135 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/23 1403/10/3 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/20 1403/12/2 Murni Nabila Mohd Zawawi Murni Nabila Mohd Zawawi Department of Electrical Engineering, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia Malaysia murninabilazawawi1996@gmail.com Zainuddin Mat Isa Zainuddin Mat Isa Centre of Excellence for Renewable Energy, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Malaysia zainuddin@unimap.edu.my Baharuddin Ismail Baharuddin Ismail Centre of Excellence for Renewable Energy, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Malaysia baha@unimap.edu.my Mohd Hafiz Arshad Mohd Hafiz Arshad Department of Electrical Engineering, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia Malaysia hafizarshad@unimap.edu.my Ernie Che Mid Ernie Che Mid Centre of Excellence for Renewable Energy, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Malaysia ernie@unimap.edu.my Md Hairul Nizam Talib Md Hairul Nizam Talib Department Electrical Engineering, Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100, Melaka, Malaysia Malaysia hairulnizam@utem.edu.my Muhammad Fitra Zambak Muhammad Fitra Zambak Department of Electrical Engineering, Universitas Muhammadiyah Sumatera Utara, 20238 Medan, Indonesia Indonesia mhdfitra@umsu.ac.id Henry Gas Solubility Optimization Algorithm Multilevel Inverter Reduced Switch Selected Harmonic Elimination. [1] N. Prabaharan and K. Palanisamy, “A comprehensive review on reduced switch multilevel inverter topologies, modulation techniques and applications,” Renewable and Sustainable Energy Reviews, vol. 76. 2017. doi: 10.1016/j.rser.2017.03.121.##[2] S. Choudhury, M. Bajaj, T. Dash, S. Kamel, and F. Jurado, “Multilevel inverter: A survey on classical and advanced topologies, control schemes, applications to power system and future prospects,” Energies (Basel), vol. 14, no. 18, 2021, doi: 10.3390/en14185773.##[3] C. S. Kumar and S. T. Kalyani, “Improvement of Power Quality in Smart Grid Connected P.V. System Using Multi-Level Inverter,” Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 31, no. 1, pp. 1–13, Jun. 2023, doi: 10.37934/araset.31.1.113.##[4] H. S. Maaroof, H. Al-Badrani, and A. T. Younis, “Design and simulation of cascaded H-bridge 5-level inverter for grid connection system based on multi-carrier PWM technique,” IOP Conf Ser Mater Sci Eng, vol. 1152, no. 1, p. 012034, May 2021, doi: 10.1088/1757-899x/1152/1/012034.##[5] M. Malinowski, K. Gopakumar, J. Rodriguez, and M. A. Perez, “A survey on cascaded multilevel inverters,” IEEE Transactions on Industrial Electronics, vol. 57, no. 7. 2010. doi: 10.1109/TIE.2009.2030767.##[6] M. Barghi Latran and A. Teke, “Investigation of multilevel multifunctional grid connected inverter topologies and control strategies used in photovoltaic systems,” Renewable and Sustainable Energy Reviews, vol. 42. 2015. doi: 10.1016/j.rser.2014.10.030.##[7] R. Castillo, B. Diong, and P. Biggers, “Single-phase hybrid cascaded H-bridge and diode-clamped multilevel inverter with capacitor voltage balancing,” IET Power Electronics, vol. 11, no. 4, pp. 700–707, Apr. 2018, doi: 10.1049/iet-pel.2017.0009.##[8] T. Qanbari and B. Tousi, “Single-Source Three-Phase Multilevel Inverter Assembled by Three-Phase Two-Level Inverter and Two Single-Phase Cascaded H-Bridge Inverters,” IEEE Trans Power Electron, vol. 36, no. 5, pp. 5204–5212, May 2021, doi: 10.1109/TPEL.2020.3029870.##[9] R. Vijayakumar, “Selective Harmonic Elimination PWM Method using Seven Level Inverters by Genetic Algorithm Optimization Technique,” International Journal of Engineering Research & Technology (IJERT), vol. 4, no. 02, 2015.##[10] G. K. Devineni and A. Ganesh, “Problem formulations, solving strategies, implementation methods & applications of selective harmonic elimination for multilevel converters,” Journal Europeen des Systemes Automatises, vol. 53, no. 6, 2020, doi: 10.18280/jesa.530620.##[11] T. Daniel Raj, M. A. Mulla, C. S. Patil, and G. A. Chougule, “A new method of selective harmonic elimination PWM for H-bridge cascaded multilevel inverter,” Int J Eng Adv Technol, vol. 9, no. 1, 2019, doi: 10.35940/ijeat.A1460.109119.##[12] M. Srndovic, A. Zhetessov, T. Alizadeh, Y. L. Familiant, G. Grandi, and A. Ruderman, “Simultaneous Selective Harmonic Elimination and THD Minimization for a Single-Phase Multilevel Inverter with Staircase Modulation,” IEEE Trans Ind Appl, vol. 54, no. 2, 2018, doi: 10.1109/TIA.2017.2775178.##[13] M. Saifizi, M. I. Fahmi, M. Othman, Wan Azani Mustafa, M. Z. Aihsan, M. R. Manan, and Azri A. Aziz., “Implementation of Two-Stage Multilevel Inverter System Using PIC Controller,” Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 28, no. 2, pp. 41–55, Oct. 2022, doi: 10.37934/araset.28.2.4155.##[14] R. Shahbaz, T. Ahmed, R. M. Elavarasan, K. Raju, M. Waqas, and U. Subramaniam, “Selective Harmonics Elimination in Multilevel Inverter Using Bio-Inspired Intelligent Algorithms,” in Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021, 2021. doi: 10.1109/AUPEC52110.2021.9597805. ##[15] M. A. Memon, S. Mekhilef, M. Mubin, and M. Aamir, “Selective harmonic elimination in inverters using bio-inspired intelligent algorithms for renewable energy conversion applications: A review,” Renewable and Sustainable Energy Reviews, vol. 82. 2018. doi: 10.1016/j.rser.2017.08.068.##[16] S. Urgun and H. Yigit, “Selective harmonic eliminated pulse width modulation (SHE-PWM) method using genetic algorithm in single-phase multilevel inverters,” International Journal on Electrical Engineering and Informatics, vol. 13, no. 1, 2021, doi: 10.15676/IJEEI.2021.13.1.11.##[17] M. T. Yaqoob, Z. Shahid, M. K. Rahmat, M. M. Alam, and M. M. Su’ud, “Selective Harmonic Elimination in Cascaded H-Bridge Multilevel Inverters using Particle Swarm Optimization: A review,” in MACS 2019 - 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics, Proceedings, 2019. doi: 10.1109/MACS48846.2019.9024783.##[18] A. Kavousi, B. Vahidi, R. Salehi, M. K. Bakhshizadeh, N. Farokhnia, and S. H. Fathi, “Application of the bee algorithm for selective harmonic elimination strategy in multilevel inverters,” IEEE Trans Power Electron, vol. 27, no. 4, pp. 1689–1696, 2012, doi: 10.1109/TPEL.2011.2166124.##[19] A. K. Koshti and M. N. Rao, “A brief review on multilevel inverter topologies,” in 2017 International Conference on Data Management, Analytics and Innovation, ICDMAI 2017, 2017. doi: 10.1109/ICDMAI.2017.8073508.##[20] F. A. Hashim, E. H. Houssein, M. S. Mabrouk, W. Al-Atabany, and S. Mirjalili, “Henry gas solubility optimization: A novel physics-based algorithm,” Future Generation Computer Systems, vol. 101, 2019, doi: 10.1016/j.future.2019.07.015.##[21] F. A. Hashim, E. H. Houssein, K. Hussain, M. S. Mabrouk, and W. Al-Atabany, “A modified Henry gas solubility optimization for solving motif discovery problem,” Neural Comput Appl, vol. 32, no. 14, pp. 10759–10771, 2020, doi: 10.1007/s00521-019-04611-0.##[22] N. Neggaz, E. H. Houssein, and K. Hussain, “An efficient henry gas solubility optimization for feature selection,” Expert Syst Appl, vol. 152, 2020, doi: 10.1016/j.eswa.2020.113364.##[23] C. S. Kumar and S. T. Kalyani, “Improvement of Power Quality Using Reduced Switch Count Eleven-Level Inverter in Smart Grid,” Journal of Advanced Research in Applied Sciences and Engineering Technology, vol. 31, no. 1, pp. 14–33, Jun. 2023, doi: 10.37934/araset.31.1.1433.##[24] M. N. M. Zawawi, Z. M. Isa, M. H. Arshad, B. Ismail, and M. H. N. Talib, “Comparative Study of Multiphase 5-Level Cascaded H-Bridge Multilevel Inverter System,” in Journal of Physics: Conference Series, Institute of Physics, 2022. doi: 10.1088/1742-6596/2312/1/012062.##[25] K. Z. Zamli, A. Kader, S. Azad, and B. S. Ahmed, “Hybrid Henry Gas Solubility Optimization Algorithm with Dynamic Cluster-to-Algorithm Mapping for Search-based Software Engineering Problems,” Neural Comput Appl, 2021, doi: https://doi.org/10.48550/arXiv.2105.14923.##[26] D. M. Utama, B. N. I. Farida, U. Fitriani, M. F. Ibrahim, and D. S. Widodo, “Hybrid Henry Gas Solubility Optimization: An Effective Algorithm for Fuel Consumption Vehicle Routing Problem,” Jurnal Ilmiah Teknik Industri, vol. 20, no. 2, pp. 141–152, Dec. 2021, doi: 10.23917/jiti.v20i2.15640.##[27] F. A. Hashim, E. H. Houssein, M. S. Mabrouk, W. Al-Atabany, and S. Mirjalili, “Henry gas solubility optimization: A novel physics-based algorithm,” Future Generation Computer Systems, vol. 101, pp. 646–667, Dec. 2019, doi: 10.1016/j.future.2019.07.015.##[28] A. Niknam Kumle, S. H. Fathi, F. Jabbarvaziri, M. Jamshidi, and S. S. Heidari Yazdi, “Application of memetic algorithm for selective harmonic elimination in multi-level inverters,” IET Power Electronics, vol. 8, no. 9, pp. 1733–1739, Sep. 2015, doi: 10.1049/iet-pel.2014.0209.##[29] M. Salman et al., “Minimization of total harmonic distortions of cascaded H-bridge multilevel inverter by utilizing bio inspired AI algorithm,” EURASIP J Wirel Commun Netw, vol. 2020, no. 1, Dec. 2020, doi: 10.1186/s13638-020-01686-5.##[30] Y. Xin, J. Yi, K. Zhang, C. Chen, and J. Xiong, “Offline Selective Harmonic Elimination with (2N+1) Output Voltage Levels in Modular Multilevel Converter Using a Differential Harmony Search Algorithm,” IEEE Access, vol. 8, pp. 121596–121610, 2020, doi: 10.1109/ACCESS.2020.3007022.##[31] A. K. Kaviani, S. H. Fathi, N. Farokhnia, and A. J. Ardakani, “PSO, an effective tool for harmonics elimination and optimization in multi-level inverters,” 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, no. June, pp. 2902–2907, 2009, doi: 10.1109/ICIEA.2009.5138740.## ##

Back Flashover Voltage on Transmission Tower of 275 KV Extra High Voltage Line (Case Study: Galang-Binjai) 2 2 Overvoltage at the insulator terminal caused by a lightning strike can occur in two ways, i.e., a direct lightning strike on the phase line and ground wire. The insulator can be exposed to the phenomenon of back flashover (BFO) if the terminal voltage of the insulator is higher than its insulator critical voltage The lightning current characteristics are distinguished by the maximum current and the steepness. Differences in the characteristics in this study are identified as International Electrical Commission (IEC) and Conseil International des Grands Reseaux Electriques (CIGRE) impulse waveform standards. The footing-tower grounding system comes in different configurations, such as horizontal, vertical, and grid. Alternative transient program (ATP) software was used for simulating lightning strikes on ground wire and phase lines. The results exhibit that the highest critical voltage of the insulators on the footing tower through grid grounding when the surge current strikes ground wire (3308kV – 3395 kV), with the magnitude of the lightning current ranging from (48 kA – 3395 kA). For lightning direct stroke on the phase line, the critical voltage on vertical grounding is highest on (2938 kV -3021 kV).  The surge current flow footing-tower is highest on the grid. The currents magnitude flow in footing tower were influenced by impedance of grounding. 136 145 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/23 1403/10/3 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/22 1403/12/4 Surya Hardi Surya Hardi Electrical Engineering Department, Universitas Sumatera Utara, Medan-Indonesia. Indonesia surya.hardi@usu.ac.id Ferry R. A. Bukit Ferry R. A. Bukit Electrical Engineering Department, Universitas Sumatera Utara, Medan-Indonesia. Indonesia Irfan Nofri Irfan Nofri Universitas Muhammadiyah Sumatera Utara, Medan- Indonesia. Indonesia Riza R. Wirasari Riza R. Wirasari Universitas Alwasliyah (Univa) Medan, Medan-Indonesia. Indonesia Muhd Hafizi Idris Muhd Hafizi Idris Centre of Excellence for Renewable Energy (CERE), Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia. Malaysia Muzamir Isa Muzamir Isa Centre of Excellence for Renewable Energy (CERE), Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia. Malaysia Back flashover voltage Lightning current String insulator footing-tower grounding and ATP software. [1] R. Bhattarai, R. Rashedin, S. Venkatesan, A. Haddad, H. Griffiths, N. Harid,”Ligthning performance of 275 kV transmission line”. 2008. 43rd International Universities Power engineering Conference. https://doi.org/10.1109/UPEC.2008.4651622##[2] R. Zoro, “Tropical Lightning Current Parameter and Protection of Transmission Line”, International Journal on Electrical engineering and Informatics, 2019, vol. 11, no. 3, pp.506-514. https://doi.org/10.15676/ijeei.2019.11.3.4##[3] D. Filipovic-Greic, B. Filipovic-Greic and Ivo Uglesic,”Lightning critical flashover of high voltage insulators Laboratory measurement and calculations”. International Review of electrical engineering (IREE), March-April 2012,vol.7, no.2, pp.4321-4328. ##[4] Silveira, F. H., and Visacro, S. “Lightning Performance of Transmission Lines: Impact of Current Waveform and Front-Time on Backflashover Occurrence”. IEEE Transactions on Power Delivery, 2019, 34(6), 2145–2151. https://doi.org/10.1109/TPWRD.2019.2897892, ##[5] S. Hardi, F. Mirza, F.R.A. Bukit, Rohana.”Influence of Ligthning Characteristics on Back Flashover in Extra High Voltage Line: A Case study”. Journal Physic Conference Journal of Physics: 2021. Conference Series, Volume 1811.https://doi.org/10.1088/1742-6596/1811/1/012048##[6] J. A. M. Velasco J. C. Araujo and S. Bedoui,” Lightning performance analysis of transmission lines using the Monte Carlo method and parallel computing”.Revista chilena de ingeniera, 2018, vol. 26, no. 3, pp. 398-408. https://doi.org/10.4067/S071833052018000300398##[7] Mobarakei, S. T., Sami, T., & Porkar, B. Back Flashover phenomenon analysis in power transmission substation for insulation coordination. 2012 11th International Conference on Environment and Electrical Engineering, EEEIC 2012 - Conference Proceedings, (2012). 170–174. https://doi.org/10.1109/EEEIC.2012.6221567##[8] M. Liu, Z. Li, R Jin Z. Zeu, C. song and R. Wang,”.Research on influences of lightning current amplitude and wave-head time to overhead line lightning over voltage” International conference on advance in material, machinery and electrical engineering. (AMMEE), 2017,pp.305- 310. https://doi.org/10.2991/ammee-17.2017.59. ##[9] S. Hardi, Hariadi, A. Fitriyani, Rohana, D. Nataliana” Transient characteristic of transmission line and tower footing grounding due to lightning strike”2023. https://doi.org/10.1063/12.0014826##[10] M. A. Abd-Allah, T. Elyan and E. Belal, ”Back flashover analysis for Egyptian 500 kV and 220 kV transmission tower”. International Journal of Scientific Research Publication. April 2016. Vol. 6, pp. 289-297. ##[11] O. S. Gouda, A. Z. El Dein and G. M.Amer.” Parameters affecting the back flashover terminal the overhead transmission line insulator caused by lightning”. Proceeding of the 14thInternational Middle East Power System Conference, Cairo, 2010. pp.44-49. ##[12] Silveira, F. H., and Visacro, S.” Lightning Performance of Transmission Lines: Methodology to Design Grounding Electrodes to Ensure an Expected Outage Rate. IEEE Trans. Power Del., Feb. 2015, vol. 30, no. 1, pp. 237-245. https://doi.org/10.1109/TPWRD.2014.2332457.##[13] Christodoulou, .A., Ekonomou, L., Papanikolaou, N., et al.: Effect of the grounding resistance to the behavior of high-voltage transmission lines surge arresters. IET Sci. Meas. Technol. (2014), 8(6), 470–478. https://doi.org/10.1049/iet-smt.2014.0017##[14] W. Anekthanasuwan, P. Jumrain, T. Jumpradit and Nit Petcharaks, ” Analysis back flashover terminal insulator string on a 115 kV transmission tower by PSCAD”. KKU Journal Engineering, July -September 2015, 42 (3), pp. 226-234. https://doi.org/10.14456/kkuenj.2015.24.##[15] A. Arismunadar, ”High Voltage Engineering” 8thed, Pradya Paramita, Indonesia, 2001, pp. 28-45.##[16] Guide for Safety in AC Substation Grounding Based on Institute of Electrical and Electronic Engineers (IEEE std 80:2000). In IEEE Standards Association, (2013), 80-2013.##[17] R. Verma and Mukhedkar D,” Fundamental consideration and impulse impedance of grounding grids’. IEEE Transactions on Power Apparatus and System, 1981, vol. 100, no.3, pp1023-1030.## ##

Impact of Temperature and Irradiance on PV Array Performance and Withstand Voltage 2 2 Photovoltaic (PV) energy is increasingly recognized as an environmentally friendly source of renewable energy. Integrating PV systems into power grids involves power electronic inverters, adding complexity and evolving traditional grids into smarter systems. Ensuring the reliability of decentralized PV generation is crucial, particularly as PV systems are often exposed to extreme weather conditions. This study investigates the impact of temperature and solar radiation on the performance of a PV array, focusing on key characteristics such as open-circuit voltage (VOC), short-circuit current (ISC), and maximum power (PMAX). Using PSCAD/EMTDC simulations, the study analyses these characteristics under varying temperatures (5°C to 45°C) and radiation levels (200 W/m² to 1200 W/m²). Results indicate that VOC increases with higher irradiance but decreases with higher temperatures. ISC increases with both higher radiation and temperature, while PMAX is optimized at high irradiance and low temperatures. The impulse withstand voltage (Vimp), a critical factor for PV system reliability, is assessed according to the PD CLC/TS 50539-12 standard. Findings reveal that at low temperatures and high radiation, the Vimp requirement is highest, emphasizing the need for robust voltage protection in PV systems. These insights underscore the importance of considering local climate conditions and implementing effective thermal management to enhance the performance and reliability of PV systems. 146 153 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/24 1403/10/4 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/26 1403/12/8 Jia Wen Tang Jia Wen Tang Centre of Excellence for Renewable Energy, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia. Malaysia wendytjw96@gmail.com Chin Leong Wooi Chin Leong Wooi Centre of Excellence for Renewable Energy, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia. Malaysia clwooi@unimap.edu.my Wen Shan Tan Wen Shan Tan School of Engineering and Advance Engineering Platform, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia. Malaysia tan.wenshan@monash.edu Nur Hazirah Zaini Nur Hazirah Zaini Power High Voltage and Energy, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, 71800 Nilai, Negeri Sembilan, Malaysia. Malaysia nurhazirah@usim.edu.my Yuan Kang Wu Yuan Kang Wu Department of Electrical Engineering, National Chung-Cheng University, Chiayi, Taiwan. Taiwan allenwu@ccu.edu.tw Syahrun Nizam bin Md Arshad@Hashim Syahrun Nizam bin Md Arshad@Hashim Centre of Excellence for Renewable Energy, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia. Malaysia Solar PV Array Temperature Radiation Impulse Withstand Voltage. [1] Panagoda, L. P. S. S., Sandeepa, R. A. H. T., Perera, W. A. V. T., Sandunika, D. M. I., Siriwardhana, S. M. G. T., Alwis, M. K. S. D., and Dilka, S. H. S. "Advancements in photovoltaic (Pv) technology for solar energy generation." Journal of research technology & engineering, vol. 4, no. 30, pp. 30-72, 2023.##[2] Zhang, Yang, Hongcai Chen, and Yaping Du. "Considerations of photovoltaic system structure design for effective lightning protection." IEEE Transactions on Electromagnetic Compatibility, vol. 62, no. 4, pp. 1333-1341, 2020. doi: 10.1109/TEMC.2020.2990930##[3] Di Bella, Alice, and Francesco Pietro Colelli. "Mitigation strategies can alleviate power system vulnerability to climate change and extreme weather: a case study on the Italian grid." Environmental Research: Infrastructure and Sustainability, vol. 5, no. 1, p. 015003, 2025. doi: 10.1088/2634-4505/ada308##[4] IPCC, 2023: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, pp. 35-115. doi: 10.59327/IPCC/AR6-9789291691647##[5] Mekhilef, Saad, Azadeh Safari, W. E. S. Mustaffa, Rahman Saidur, Rosli Omar, and M. A. A. Younis. "Solar energy in Malaysia: Current state and prospects." Renewable and Sustainable Energy Reviews, vol. 16, no. 1, pp. 386-396, 2012. doi: 10.1016/j.rser.2011.08.003##[6] Kirpichnikova, Irina M., and Ilkhom B. Makhsumov. "The influence of ambient temperature on the energy characteristics of solar modules." In 2020 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), pp. 1-5. IEEE, 2020. doi: 10.1109/ICIEAM48468.2020.9112064##[7] Chegaar, Mohamed, Amer Hamzaoui, Aboubacar Namoda, Pierre Petit, Michel Aillerie, and Axel Herguth. "Effect of illumination intensity on solar cells parameters." 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Advanced Control Strategies for Managing Circulating Currents in Islanded Microgrid Inverters 2 2 In islanded microgrids, circulating currents among parallel inverters pose significant challenges to system stability and efficient power distribution. Traditional droop control methods often struggle to manage these currents effectively, leading to inefficiencies and potential system damage. This study introduces an advanced fuzzy-robust droop control strategy that integrates fuzzy logic with robust droop control to address these challenges. By incorporating fuzzy logic, the proposed strategy enhances the adaptability of droop control to varying system conditions, improving the management of circulating currents and ensuring more accurate power sharing among inverters. Comprehensive mathematical modeling and extensive simulation analyses validate the performance of this control strategy. The results show that the fuzzy-robust droop control method significantly outperforms conventional approaches, achieving up to a 70% reduction in circulating currents. This improvement leads to a substantial reduction in power losses and enhances the dynamic response under varying load conditions. Additionally, the strategy improves voltage and frequency regulation, contributing to the overall stability and reliability of the microgrid. The findings provide a robust solution to the longstanding issue of circulating currents, optimizing microgrid operations, and paving the way for more efficient and resilient distributed energy systems. The advanced control strategy presented in this study not only addresses critical challenges but also demonstrates the potential for innovative methodologies to meet the growing demands of future energy infrastructures, where reliability and efficiency are essential. 154 162 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/24 1403/10/4 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/20 1403/12/2 Nurul Husna Abd Wahab Nurul Husna Abd Wahab Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia husnawahab@unimap.edu.my Mohd Hafizuddin Mat Mohd Hafizuddin Mat Faculty of Electronic Engineering & Technology Advanced Communication Engineering Centre of Excellence, Universiti Malaysia Perlis, Perlis, Malaysia. Malaysia hafizuddinmat@unimap.edu.my Norezmi Md Jamal Norezmi Md Jamal Department of Electrical Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Malaysia. Malaysia norezmi@uthm.edu.my Nur Hidayah Ramli Nur Hidayah Ramli Faculty of Electronic Engineering & Technology Advanced Communication Engineering Centre of Excellence, Universiti Malaysia Perlis, Perlis, Malaysia. Malaysia hidayahramli@unimap.edu.my Circulating current Fuzzy-Robust control islanded microgrid power-sharing fuzzy logic robust droop [1] G. Invernizzi and G. Vielmini, “Challenges in Microgrid Control Systems Design. An application case,” in 2018 AEIT International Annual Conference, 2018, pp. 1–6.##[2] J. VAISH, A. K. Tiwari, and S. K., “Cost Optimization and Battery Sizing of Grid-Connected Microgrid with Distributed Energy Resources Using Random Forest Technique,” Iran. J. Electr. Electron. Eng., vol. 19, no. 4, 2023.##[3] R. Sabzehgar, “Overview of Technical Challenges, Available Technologies and Ongoing Developments of AC/DC Microgrids,” in Development and Integration of Microgrids, W.-P. Cao and J. Yang, Eds. Rijeka: InTech, 2017.##[4] A. W. N. Husna, M. A. Roslan, and M. H. Mat, “Droop control technique for equal power sharing in islanded microgrid,” Int. J. Power Electron. Drive Syst., vol. 10, no. 1, 2019.##[5] M. Syahril, M. A. Roslan, and B. Ismail, “Microgrid synchronization using power offset through a central controller,” in Journal of Physics: Conference Series, 2020, vol. 1432, no. 1.##[6] A. Karimpour, A. M. Amani, M. Karimpour, and M. Jalili, “Enhancing Voltage Regulation in DC Microgrids Using a Price Incentive Load Management Approach,” Iran. J. Electr. Electron. Eng., vol. 17, no. 4, 2021.##[7] S. P. Tiwari, “A Dependable Protection Scheme for Electrical Vehicle Integrated Microgrid Considering Stressed Fault Scenarios and Dissimilar Fault Inceptions,” Iran. J. Electr. Electron. Eng., vol. 20, no. 1, 2024.##[8] A. W. N. Husna, S. F. Siraj, and M. H. Mat, “Effect of load variations in DC-DC converter,” in Proceedings - CIMSim 2011: 3rd International Conference on Computational Intelligence, Modelling and Simulation, 2011.##[9] N. H. A.W., S. S.F., and M. Z. Ab Muin, “Modeling of DC-DC converter for solar energy system applications,” in 2012 IEEE Symposium on Computers & Informatics (ISCI), 2012, pp. 125–129.##[10] V.-H. Bui, A. Hussain, and H.-M. Kim, “A Strategy for Flexible Frequency Operation of Stand-Alone Multimicrogrids,” IEEE Trans. Sustain. Energy, vol. 9, no. 4, pp. 1636–1647, 2018.##[11] Q. Liu, T. Caldognetto, and S. Buso, “Review and Comparison of Grid-Tied Inverter Controllers in Microgrids,” IEEE Trans. Power Electron., vol. 35, no. 7, pp. 7624–7639, 2020.##[12] N. H. A. Wahab, M. H. Mat, and M. A. Roslan, “A Review on Optimization of Control Strategy on Paralleled Connected Inverters in an Islanded Microgrid,” Adv. Sci. Lett., vol. 23, no. 6, pp. 5406–5409, Jun. 2017.##[13] R. WANG, Q. SUN, Y. GUI, and D. MA, “Exponential-function-based droop control for islanded microgrids,” J. Mod. Power Syst. Clean Energy, vol. 7, no. 4, pp. 899–912, 2019.##[14] E. De Santis, A. Rizzi, and A. Sadeghian, “A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids,” Apr. 2016.##[15] Y. Xia, Y. Peng, P. Yang, M. Yu, and W. Wei, “Distributed Coordination Control for Multiple Bidirectional Power Converters in a Hybrid AC/DC Microgrid,” IEEE Trans. Power Electron., vol. 32, no. 6, pp. 4949–4959, 2017.##[16] S. Gholami, S. Saha, and M. Aldeen, “Robust multiobjective control method for power sharing among distributed energy resources in islanded microgrids with unbalanced and nonlinear loads,” Int. J. Electr. Power Energy Syst., vol. 94, pp. 321–338, Jan. 2018.##[17] K. Wang, X. Huang, B. Fan, Q. Yang, G. Li, and M. L. Crow, “Decentralized Power Sharing Control for Parallel-Connected Inverters in Islanded Single-Phase Micro-Grids,” IEEE Trans. Smart Grid, vol. 9, no. 6, pp. 6721–6730, Nov. 2018.##[18] F. Deng, A. Petucco, P. Mattavelli, and X. Zhang, “An enhanced current sharing strategy for islanded ac microgrids based on adaptive virtual impedance regulation,” Int. J. Electr. Power Energy Syst., vol. 134, p. 107402, 2022.##[19] Q. C. Zhong, Y. Wang, and B. Ren, “UDE-Based Robust Droop Control of Inverters in Parallel Operation,” IEEE Trans. Ind. Electron., vol. 64, no. 9, pp. 7552–7562, 2017.##[20] Z. Peng et al., “Droop control strategy incorporating coupling compensation and virtual impedance for microgrid application,” IEEE Trans. Energy Convers., vol. 34, no. 1, pp. 277–291, 2019.##[21] A. Villalon, C. Munoz, R. Aliaga, J. Munoz, M. Rivera, and P. Zanchetta, “Power sharing control of islanded ac microgrid considering droop control and virtual impedance,” Proc. IEEE Int. Conf. Ind. Technol., vol. 2020-Febru, pp. 1139–1144, 2020.##[22] J. Niu et al., “Analysis of circulating harmonic currents in paralleled three level ANPC inverters using SVM,” Conf. Proc. - IEEE Appl. Power Electron. Conf. Expo. - APEC, vol. 2019-March, pp. 2481–2487, 2019.##[23] B. Wei, J. M. Guerrero, J. C. Vasquez, and X. Guo, “A circulating current suppression method for parallel connected voltage-source-inverters (VSI) with common DC and AC buses,” in 2016 IEEE Energy Conversion Congress and Exposition (ECCE), 2016, pp. 1–6.##[24] A. Karaarslan and M. E. Seker, “Distributed Control of Microgrids,” Power Syst., no. February, pp. 403–422, 2020.##[25] Q. C. Zhong and D. Boroyevich, “Structural Resemblance between Droop Controllers and Phase-Locked Loops,” IEEE Access, vol. 4, pp. 5733–5741, 2016.##[26] X. Wang, H. Lv, Q. Sun, Y. Mi, and P. Gao, “A proportional resonant control strategy for efficiency improvement in extended range electric vehicles,” Energies, vol. 10, no. 2, 2017.##[27] Asian Development Bank, Handbook on Microgrids for Power Quality and Connectivity, no. July. 2020.##[28] A. Kaur, J. Kaushal, and P. Basak, “Areview on microgrid central controller,” Renew. Sustain. Energy Rev., vol. 55, pp. 338–345, Mar. 2016.##[29] M. U. Safder, M. J. Sanjari, A. Hamza, R. Garmabdari, M. A. Hossain, and J. Lu, “Enhancing Microgrid Stability and Energy Management: Techniques, Challenges, and Future Directions,” Energies, vol. 16, no. 18, 2023.##[30] Q.-C. Zhong, “Robust Droop Controller for Accurate Proportional Load Sharing Among Inverters Operated in Parallel,” IEEE Trans. Ind. Electron., vol. 60, no. 4, pp. 1281–1290, Apr. 2013.## ##

Impact of Nonlinear and Unbalanced Loads on Neutral Conductors in Three-Phase Systems: Modelling and Simulation Analysis 2 2 The rise of nonlinear and unbalanced loads in modern electrical systems poses challenges to power quality management. These loads, prevalent in electronic devices and industrial equipment, induce harmonic distortions and unbalance, adversely affecting the neutral conductor in three-phase systems. This study investigates these effects through modeling and simulation using MATLAB/Simulink and symmetrical components theory for detailed power quality analysis. The research focuses on three scenarios: nonlinear loads, unbalanced loads, and combined nonlinear-unbalanced loads. Simulation results show that nonlinear loads significantly increase harmonic content, while unbalanced loads lead to notable power quality deviations. When combined, these conditions exacerbate harmonic distortions and unbalance, resulting in higher neutral current magnitudes. Key findings highlight the severe impact of combined load conditions on the neutral conductor, emphasizing the need for accurate modeling and analysis. This research provides valuable insights and practical recommendations for addressing the challenges of nonlinear and unbalanced loads, contributing to improved power system design and management. 163 172 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/242024/12/25 1403/10/5 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/202025/02/22 1403/12/4 Mohd Zulhisham Mohd Radzi Mohd Zulhisham Mohd Radzi Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Malaysia mzulhisham@unimap.edu.my Baharuddin Ismail Baharuddin Ismail Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Malaysia baha@unimap.edu.my Muhammad Mokhzaini Azizan Muhammad Mokhzaini Azizan Department of Electrical & Electronic Engineering, Faculty of Engineering and Build Environment, Universiti Sains Islam Malaysia, Bandar Baru Nilai, 71800 Nilai, Negeri Sembilan, Malaysia Malaysia mokhzainiazizan@usim.edu.my Power Quality Unbalanced Load Nonlinear Load neutral Current [1] A. D. Martin, R. S. Herrera, J. R. Vazquez, P. Crolla, and G. M. Burt, “Unbalance and harmonic distortion assessment in an experimental distribution network,” Electr. Power Syst. Res., vol. 127, pp. 271–279, 2015.##[2] B. Arabsalmanabadi, A. Javadi, and K. Al-haddad, “Harmonic power flow in unbalanced and polluted radial distribution systems,” 2017 IEEE Int. Conf. Ind. Technol., pp. 1504–1509, 2017.##[3] P. A. Valand, F. Talajiya, and I. M. Desai, “Power Quality Enhancement by Mitigating Current Harmonics in Power System Network using D-STATCOM,” in Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022, 2022, pp. 1727–1734. ##[4] A. P. SK and P. kumar N, “Review of Power Quality Issues and Mitigation Techniques in Electrical Power Systems,” Int. J. Eng. Technol. Manag. Sci., vol. 4, no. 5, pp. 116–120, Sep. 2020.##[5] G. Kolap, S. U. Bagwan, P. Chougule, B. Ghule, and N. Nangare, “Harmonic mitigation by shunt passive power filter at voltage source type non-linear load,” 2020 5th Int. Conf. Commun. Electron. Syst., pp. 84–89, 2020.##[6] V. Utyuzhnikova, E. Borisova, and E. Boloev, “BALANCING OF PHASE LOADS IN LOW VOLTAGE THREE-PHASE FOUR-WIRE NETWORKS,” Sci. Pap. Collect. Angarsk State Tech. Univ., vol. 2023, no. 1, pp. 244–251, Jul. 2023.##[7] A. Ojo, K. O. Awodele, and A. Sebitosi, “Load Compensation in a Three-Phase Four Wire Distribution System Considering Unbalance, Neutral Current Elimination and Power Factor Improvement,” 2019 South. African Univ. Power Eng. Conf. Mechatronics/Pattern Recognit. Assoc. South Africa, pp. 389–394, 2019.##[8] C. K. Chang, S. T. Cheng, and B. K. Boyanapalli, “Three-Phase Unbalance Improvement for Distribution Systems Based on the Particle Swarm Current Injection Algorithm,” Energies, vol. 15, no. 9, pp. 3460–3460, May 2022.##[9] B. G. Lemma, M. Laio, Duan, and Xiongying, “Second-Order Odd Repetitive Control for Three-Phase Four-Wire Active Power Filter to Mitigation Current Harmonics, Unbalance and Neutral Current of Nonlinear Loads,” Int. J. Eng. Res., vol. 9, 2020.##[10] M. Z. Mohd Radzi, M. M. Azizan, and B. Ismail, “Observatory case study on total harmonic distortion in current at laboratory and office building,” J. Phys. Conf. Ser., vol. 1432, no. 1, p. 12008, 2020.##[11] B. Acarkan and K. Erkan, Harmonics Modeling and Harmonic Activity Analysis of Equipments with Switch Mode Power Supply using MATLAB and Simulink, vol. 1. 2007.##[12] N. Dey and A. Chakraborty, “Neutral Current and Neutral Voltage in Three Phase Four Wire Distribution System of a Technical Institution,” Int. J. Comput. Appl., vol. 72, pp. 1–7, Jun. 2013.##[13] M. Bajaj and A. K. Singh, “Increasing renewable energy penetration in harmonically polluted distribution grids using passive filtering: a comparative assessment of common filter types,” Electr. Eng., vol. 104, no. 5, pp. 2979–3005, 2022.##[14] O. Mahela and A. Shaik, “Power Quality Improvement in Distribution Network using DSTATCOM with Battery Energy Storage System,” Int. J. Electr. Power Energy Syst., vol. 83, pp. 229–240, Dec. 2016.##[15] A. A. Belitskiy, I. I. Rastvorova, and O. V Denisova, “Nonlinear and unbalanced load as a basic factor of a neutral conductor current,” 2018 IEEE Conf. Russ. Young Res. Electr. Electron. Eng., pp. 570–571, 2018.##[16] J. Chen, T. Yang, C. O’Loughlin, and T. O’Donnell, “Neutral Current Minimization Control for Solid State Transformers Under Unbalanced Loads in Distribution Systems,” IEEE Trans. Ind. Electron., vol. 66, pp. 8253–8262, 2019.##[17] A. Vinayagam, K. Swarna, S. Y. Khoo, and A. Stojcevski, “Power Quality Analysis in Microgrid: An Experimental Approach,” J. Power Energy Eng., vol. 04, no. 04, pp. 17–34, 2016.##[18] M. VIJAYAKUMAR and S. VIJAYAN, “Design and implementation of PV-based three-phase four-wire series hybrid active power filter for power quality improvement,” Sadhana, vol. 39, no. 4, pp. 859–877, 2014.##[19] R. Li, P. Wong, K. Wang, B. Li, and F. Yuan, “Power quality enhancement and engineering application with high permeability distributed photovoltaic access to low-voltage distribution networks in Australia,” Prot. Control Mod. Power Syst., vol. 5, no. 1, p. 18, 2020.##[20] M. Bollen and F. Hassan, “Power Quality Disturbances,” in Integration of Distributed Generation in the Power System, Wiley, 2011, pp. 223–298.##[21] J. Chen, T. Yang, C. O’Loughlin, and T. O’Donnell, “Neutral current minimization control for solid state transformers under unbalanced loads in distribution systems,” IEEE Trans. Ind. Electron., vol. 66, no. 10, pp. 8253–8262, 2019.##[22] A. Junaidi, Rahmaniar, R. Salman, J. S. Rambey, and Baharuddin, “Modelling and simulation of reduce harmonic distortion in non-linear loads,” Adv. Sci. Technol. Eng. Syst., vol. 5, no. 5, pp. 364–369, 2020.##[23] J. Pan, J. Liu, X. Chen, and K. Zhong, “Three-phase unbalanced load control based on load–electricity transfer index,” Energy Reports, vol. 7, pp. 312–318, 2021.##[24] P. Bloomfield, Fourier Analysis of Time Series. Wiley, 2000.##[25] IEEE Std 1159, IEEE Recommended Practice for Monitoring Electric Power Quality, vol. 1995, no. 26 June. 2009.##[26] International Electrotechnical Commission (IEC), “61000-4-7: General guide on harmonics and interharmonics measurements for power supply systems and equipment connected thereto.,” Int. Electrotech. Comm. CH-1211 Geneva 20, vol. 2.1, 2009.##[27] TENGA CABLE INDUSTRIES SDN. BHD., “XLPE Insulated Cables,” 1994. https://www.tcisb.com.my/wp-content/uploads/2017/03/low-voltage.pdf## ##

Ultra-high Frequency Sensor for Partial Discharges Detection in High Voltage Substation 2 2 The development of advanced diagnostic tools is critical for the effective monitoring and management of electrical insulation systems. This paper presents the development of an Ultra High Frequency (UHF) sensor designed for the detection of partial discharges (PD) within high-voltage substations. The study focuses on the sensor’s technical development, encompassing design considerations, fabrication processes, and initial performance evaluations in laboratory settings. The engineering principles underlying the sensor design are detailed, including the selection of innovative materials that enhance sensitivity and frequency response. The sensor configuration is tailored to optimize the detection of PD signals, with adjustments made based on simulated PD scenarios. Initial testing results demonstrate the sensor’s capability to detect a range of PD activities, showcasing its potential effectiveness in real-world applications. The sensor's performance is analyzed through a series of controlled lab experiments, which confirm its high sensitivity and broad operational frequency range. This paper not only illustrates the technical specifications and capabilities of the newly developed UHF sensor but also discusses its practical implications for improving the reliability and efficiency of PD monitoring systems in electrical substations. 173 181 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/242024/12/252024/12/25 1403/10/5 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/202025/02/222025/02/22 1403/12/4 Sharulnizam Mohd Mukhtar Sharulnizam Mohd Mukhtar Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau Perlis, Malaysia. Malaysia sharul@unimap.edu.my MUZAMIR ISA MUZAMIR ISA Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau Perlis, Malaysia Malaysia muzamir@unimap.edu.my Azremi Abdullah Al-Hadi Azremi Abdullah Al-Hadi Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau Perlis, Malaysia. Malaysia Partial Discharge UHF Sensor Antipodal Vivaldi Antenna. [1] Cichecki, Piotr, Edward Gulski, J.J. Smit, T. Hermans, R. Bodega, and P.P. Seitz. 2009. “Conventional and Unconventional Partial Discharges Detection in Power Cables Using Different AC Voltages.” In 2009 IEEE Electrical Insulation Conference, 5–9. https://doi.org/10.1109/EIC.2009.5166316.##[2] Uwiringiyimana, Jean Pierre, Umar Khayam, Suwarno, and Gian Carlo Montanari. 2022. “Comparative Analysis of Partial Discharge Detection Features Using a UHF Antenna and Conventional HFCT Sensor.” IEEE Access 10:107214–26. https://doi.org/10.1109/ACCESS.2022.3212746.##[3] Kalathiripi, Hussain, and Subrata Karmakar. 2017. “Partial Discharge Study in Transformer Oil Using Acoustic Emission Technique and UV-Visible Spectroscopy.” In 2017 3rd International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), 361–66. https://doi.org/10.1109/CATCON.2017.8280245.##[4] Xie, Qing, Shuyi Cheng, Fangcheng Fangcheng Lü, and Yanqing Li. 2013. “Location of Partial Discharge in Transformer Oil Using Circular Array of Ultrasonic Sensors.” IEEE Transactions on Dielectrics and Electrical Insulation 20 (5): 1683–90. https://doi.org/10.1109/TDEI.2013.6633698.##[5] Liu, Zhiheng, Yongqing Wang, Xiao Chen, Xiaokai Meng, Xiuling Liu, and Jianquan Yao. 2021. “An Optical Fiber Sensing Method for Partial Discharge in the HVDC Cable System.” International Journal of Electrical Power & Energy Systems 128:106749. https://doi.org/10.1016/j.ijepes.2020.106749.##[6] Mehmood, Muhammad Ali, Jian Li, Huang Zhengyong, Feipeng Wang, M. Shoaib Bhutta, Xudong Li, and Jawad Ahmad. 2018. “Study of Field Aged Transformer Insulation Oil Properties Using GC-MS.” In 2018 IEEE International Conference on High Voltage Engineering and Application (ICHVE), 1–4. https://doi.org/10.1109/ICHVE.2018.8642174.##[7] Liang, Rui, Shenglei Wu, Peng Chi, Nan Peng, and Yi Li. 2019. "Optimal Placement of UHF Sensors for Accurate Localization of Partial Discharge Source in GIS" Energies 12, no. 6: 1173. https://doi.org/10.3390/en12061173.##[8] Jahangir, Hamid, Asghar Akbari, Peter Werle, Mohammad Akbari, and Janusz Szczechowski. 2017. “UHF Characteristics of Different Types of PD Sources in Power Transformers.” In 2017 Iranian Conference on Electrical Engineering (ICEE), 1242–47. https://doi.org/10.1109/IranianCEE.2017.7985232.##[9] Álvarez, Fernando, Fernando Garnacho, Javier Ortego, and Miguel Ángel Sánchez-Urán. 2015. "Application of HFCT and UHF Sensors in On-Line Partial Discharge Measurements for Insulation Diagnosis of High Voltage Equipment" Sensors 15, no.4:7360-7387. https://doi.org/10.3390/s150407360.##[10] Ma, Chenxi, Han Li, Wenjun Zhou, Jianhui Yu, Lingzhi Wang, Shuai Yang, and Shizhuo Hu. 2018. “Background Noise of Partial Discharge Detection and Its Suppression in Complex Electromagnetic Environment.” In 2018 IEEE International Conference on High Voltage Engineering and Application(ICHVE),1–4. https://doi.org/10.1109/ICHVE.2018.8642084.##[11] Chai, Hua, B.T. Phung, and Steve Mitchell. 2019. "Application of UHF Sensors in Power System Equipment for Partial Discharge Detection: A Review" Sensors 19, no. 5: 1029. https://doi.org/10.3390/s19051029.##[12] Dennis, Graham, Miles Redfern, and Steve Pennock. 2006. “The Development of Non-Intrusive Techniques for Partial Discharge Detection on MV and EHV Switchgear.” In Proceedings of the 41st International Universities Power Engineering Conference, 3:814–18. https://doi.org/10.1109/UPEC.2006.367593.##[13] Reid, Alistair. 2009. “Monitoring of Partial Discharge in Substation Equipment Using a Novel Multi-Sensor Cable Loop.” In CIRED 2009 - The 20th International Conference and Exhibition on Electricity Distribution - Part 2, 1–18. https://doi.org/10.1049/cp.2009.1005.##[14] Kurrer, R., and K. Feser. 1998. “The Application of Ultra-High-Frequency Partial Discharge Measurements to Gas-Insulated Substations.” IEEE Transactions on Power Delivery 13 (3): 777–82. https://doi.org/10.1109/61.686974.##[15] Pinpart, T., and M. D. Judd. 2009. “Experimental Comparison of UHF Sensor Types for PD Location Applications.” In 2009 IEEE Electrical Insulation Conference, 26–30. https://doi.org/10.1109/EIC.2009.5166319.##[16] Park, Seungyong, and Kyung-Young Jung. 2020. “Design of a Circularly-Polarized UHF Antenna for Partial Discharge Detection.” IEEE Access 8:81644–50. https://doi.org/10.1109/ACCESS.2020.2991158.##[17] Pei, Yan, Zheng Qian, Jixi Lu, Fengtao Liu, and Xiaohu Zhao. 2015. “Development of UHF PD Monitoring System Based on FPGA.” In 2015 IEEE 11th International Conference on the Properties and Applications of Dielectric Materials (ICPADM),999–1002. https://doi.org/10.1109/ICPADM.2015.7295443.##[18] Sinaga, H. H., B. T. Phung, P. L. Ao, and T. R. 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Development of a Digital Stethoscope for Enhancing Real-Time Respiratory Diagnostics 2 2 Digital stethoscopes represent a significant advancement in medical diagnostics, addressing the limitations of traditional auscultation methods, which often suffer from diagnostic delays and inefficient workflows. This digital stethoscope facilitates real-time diagnosis through machine learning and remote monitoring, utilizing the ESP32’s ADC and Wi-Fi capabilities to wirelessly send audio data to a remote server for comprehensive analysis. By integrating modern technologies such as the ESP32 microcontroller and the MAX9814 microphone module, these devices capture and transmit high-fidelity respiratory sounds, overcoming the challenges of imprecision and time lag in conventional methods. Initial tests have demonstrated the device's ability to capture clear respiratory sounds, underscoring its potential for effective remote health monitoring and telemedicine. These improvements aim to enhance diagnostic accuracy, facilitate early diagnosis, and ultimately improve patient outcomes, showcasing the significant potential of digital stethoscopes to transform respiratory diagnostics and patient care, particularly in remote and telemedicine settings. In this research, a prototype of a digital stethoscope for respiratory diagnostics was developed and evaluated. The obtained results from the prototype measurements demonstrated that the proposed system could be a solid starting point for the actual implementation of an advanced respiratory monitoring system. 182 192 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/242024/12/252024/12/252024/12/26 1403/10/6 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/202025/02/222025/02/222025/02/22 1403/12/4 JULIE ROSLITA RUSLI JULIE ROSLITA RUSLI Department of Electronics Technology, British Malaysian Institute Campus, Universiti Kuala Lumpur, Gombak, Selangor. Malaysia julie@unikl.edu.my Muhamad Syahirin Danial Noor Shahrin Muhamad Syahirin Danial Noor Shahrin Department of Electronics Technology, British Malaysian Institute Campus, Universiti Kuala Lumpur, Gombak, Selangor. Malaysia syahirir.shahrin@s.unikl.edu.my Nurul Izzati Binti Che Abdu Patah Nurul Izzati Binti Che Abdu Patah Department of Electronics Technology, British Malaysian Institute Campus, Universiti Kuala Lumpur, Gombak, Selangor. Malaysia izzati.abduulie@s.unikl.edu.my Izanoordina Ahmad Izanoordina Ahmad Department of Electronics Technology, British Malaysian Institute Campus, Universiti Kuala Lumpur, Gombak, Selangor. Malaysia izanoordina@unikl.edu.my Siti Marwangi Mohamad Maharum Siti Marwangi Mohamad Maharum Department of Electronics Technology, British Malaysian Institute Campus, Universiti Kuala Lumpur, Gombak, Selangor. Malaysia sitimarwangi@unikl.edu.my Sairul Izwan Safie Sairul Izwan Safie Department of Plan Engineering Technology, Malaysia Institute of Industrial Technology, Universiti Kuala Lumpur, Johor Baharu, Johor, Malaysia. Malaysia sairulizwan@unikl.edu.my Digital stethoscope Respiratory diagnostics Remote health monitoring [1] D. M. Huang, J. Huang, K. Qiao, et al., "Deep learning-based lung sound analysis for intelligent stethoscope," Military Med. Res., vol. 10, no. 44, 2023. doi: 10.1186/s40779-023-00479-3.##[2] S. H. Lee, Y. S. Kim, M. K. Yeo, M. Mahmood, N. Zavanelli, C. Chung, J. Y. Heo, Y. Kim, S. S. Jung, and W. H. Yeo, "Fully portable continuous real-time auscultation with a soft wearable stethoscope designed for automated disease diagnosis," Sci. Adv., vol. 8, no. 21, May 2022.##[3] J. Zhao and S. Wang, "A Review of Respiratory Sound Signal Processing Techniques for Disease Detection," IEEE Access, vol. 8, pp. 155206-155222, 2020. doi: 10.1109/ACCESS.2020.3011850.##[4] S. Chowdhury, A. B. M. S. U. Doulah, and M. Rasheduzzaman, "Quality Assessment of Respiratory Sounds Extracted from Self-Assembled Digital Stethoscopes," in Proc. IEEE Conf. on Electrical and Electronics Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh, 2022, pp. 45-51.##[5] R. Chitra, N. Jayapreetha, D. Swetha, and S. Swetha, "Digital Stethoscope for Instant Monitoring for Cardiac Auscultation," in Proc. Int. Conf. on Electrical Engineering and Computer Science, Sri Sairam Engineering College, Chennai, India, 2023, pp. 123-130.##[6] M. Waqar, S. Inam, et al., "Arduino Based Cost-Effective Design and Development of a Digital Stethoscope," J. Med. Devices, vol. 13, no. 4, pp. 678-686, Oct. 2019.##[7] P.-W. F. Lo and M. Q.-H. Meng, "A Low-Cost Bluetooth Powered Wearable Digital Stethoscope for Cardiac Murmur," J. Healthc. Eng., vol. 8, no. 2, pp. 345-356, Jul. 2017.##[8] M. Saleh, N. Ibrahim, and D. Ramli, "Data reduction on MFCC features based on kernel PCA for speaker verification system," WALIA Journal, vol. 30, no. 2, pp. 56-62, 2014.##[9] G. M. M. Alshmrani, Q. Ni, R. Jiang, H. Pervaiz, and N. M. Elshennawy, "A Deep Learning Architecture for Multi-Class Lung Diseases Classification Using Chest X-Ray (CXR) Images," Alexandria Eng. J., vol. 64, pp. 923-935, 2023.##[10] A. Nguyen, J. Yosinski, and J. Clune, "Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2015, pp. 427-436.##[11] A. Kendall and Y. Gal, "What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?," in Adv. Neural Inf. Process. Syst., 2017, pp. 5574-5584.##[12] J. R. Rusli, S. Shafie, W. Z. W. Hassan, H. A. Majid, I. Ahmad, and M. A. Mustafa, "A Post-Silicon Validation Method for Low-Power 180 nm Dynamic Comparator in Differential 10-bit SAR ADC," in Proc. IEEE 9th Int. Conf. on Smart Instrumentation, Measurement and Applications (ICSIMA), Kuala Lumpur, Malaysia, 2023, pp. 199-204.##[13] S. Liang, Y. Li, and R. Srikant, "Enhancing the Reliability of Out-of-Distribution Image Detection in Neural Networks," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2018, pp. 8578-8586.##[14] X. Wang, Z. Zhang, and Y. Zhang, "Comparison of Sequential and Simultaneous Acoustic Measurement Techniques for Respiratory Sounds," J. Biomed. Eng., vol. 42, no. 4, pp. 1152-1163, 2019. doi: 10.1016/j.jbiomech.2019.03.014.##[15] S. Kwon, J. Park, and S. Lee, "Optimal Sampling Rate and Feature Extraction for Real-time Respiratory Sound Analysis," Sensors, vol. 21, no. 18, p. 6134, 2021.##[16] Y. W. Kuo, Y. C. Tsao, W. C. Chien, Y. M. Huang, and L. D. Liao, "Smart health monitoring and management system for organizations using radio-frequency identification (RFID) technology in hospitals or emergency applications," Emerg. Med. Int., vol. 2022, p. 2177548, 2022.##[17] L. B. Tolle, "Challenges in the Diagnosis and Management of Patients with Fibrosing Interstitial Lung Disease," Case Rep. Pulmonol., vol. 2022, p. 9942432, Feb. 2022. doi: 10.1155/2022/9942432.##[18] Y.-W. Ju, C. Hui, and Lun, "IoT-based wearable health monitoring device and its validation for potential critical and emergency applications," Front. Public Health, vol. 11, 2023.##[19] M. S. Kamel, J. L. Davidson, and M. S. Verma, "Strategies for Bovine Respiratory Disease (BRD) Diagnosis and Prognosis: A Comprehensive Overview," Animals, vol. 14, no. 4, p. 627, 2024.##[20] R. Wang and M. S. Bonney, "Novel Data Acquisition Utilizing a Flask Python Digital Twin Operational Platform," in Special Topics in Structural Dynamics & Experimental Techniques, M. Allen, S. Davaria, and R. B. Davis, Eds., Cham: Springer, 2023.## ##

Design and Performance Evaluation of a Novel Time Measurement Calibration Device for Electric Power Systems 2 2 In order to solve the difficulty of digital signal calibration of electric power equipment, such as low precision, inability to test the full range, and complicated configuration, and further promote the development of power system, a proposed time measurement calibration device is designed, and its performance is verified in this paper. This paper points out the main drawbacks of the existing calibration system, carries on the design innovation of the key technologies based on FPGA (Field Programmable Gate Array), puts forward the optimization method of the software and hardware, and verifies the accuracy of the input and output signal by experiments. The accuracy of input and output SV, GOOSE, and contact signal of the proposed calibration device in this paper can be better than 10μs, which is a meaningful improvement in accuracy and efficiency for time measurement calibration. 193 202 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/242024/12/252024/12/252024/12/262024/12/26 1403/10/6 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/202025/02/222025/02/222025/02/222025/02/22 1403/12/4 Huang Yan Huang Yan Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching 93350, Sarawak, Malaysia. Malaysia yhuang@swinburne.edu.my hadi nabipour afrouzi hadi nabipour afrouzi College of Engineering, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham B4 7XG, West Midlands, England, United Kingdom. United Kingdom hadi.nabipourafrouzi@bcu.ac.uk Chin-Leong Wooi Chin-Leong Wooi HVTrans, Centre of Excellence for Renewable Energy (CERE), Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, Arau, Perlis, Malaysia. Malaysia clwooi@unimap.edu.my Hieng Tiong Su Hieng Tiong Su Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching 93350, Sarawak, Malaysia. Malaysia hsu@swinburne.edu.my Ismat Hijazin Ismat Hijazin School of Engineering, Swinburne University of Technology, Melbourne, Australia. Australia ihijazin@swin.edu.au Electric power equipment time measurement calibration Field Programmable Gate Array [1] Xiao H., “Evaluation of Uncertainty of Measurement Results of Optical Relay Protection Test equipment”, Electronics Quality, vol. 415, no.10, pp. 62-66, Oct. 2021.##[2] Chen G. H., Wang P. F., Zhao Y. B., Zheng P., Mu X. L. and Huang Y., “Automatic test equipment design of intelligent terminals in a smart substation”, Power System Protection and Control, vol. 49, no.17, pp. 162-169, 2021. https://link.cnki.net/doi/10.19783/j.cnki.pspc.201329##[3] You C. X., Wang C. Y. and Fang C. G., “Research and application of automatic test system for relay protection device in intelligent substation”, Electronic Test, vol. 03, pp. 113-115, 2022. https://link.cnki.net/10.16520/j.cnki.1000-8519.2022.03.026##[4] Pan B., Wei C., Gui X. Z. and Zhang L. P., “Development and application of a portable arc protection tester”, Power System Protection and Control, vol. 48, no.13, pp. 149-155, 2020. https://link.cnki.net/doi/10.19783/j.cnki.pspc.190969##[5] Zeng W., “Research on the way of relay power protection tripping of intelligent substation system”, Electric Power Equipment Management, vol. 6, pp. 41-44, 2019.##[6] Yao Z. Q., Chen G. H. and Wang P. F., “Analysis and solution for the influence of DC bias of a merging unit on bus protection”, Power System Protection and Control, vol. 49, no.18, pp. 167-172, 2021. https://link.cnki.net/doi/10.19783/j.cnki.pspc.201522##[7] Hou J. W., Jiang T. Y., Fan R. N., Gao R. S. and Zhang X., “Design of a New Type of Relay Protection Test Equipment”, China Science and Technology Information, vol. 6, no. 24, pp. 83-85, Mar. 2023.##[8] Wang J. H., Wei F., Liu Y. W., Li W. D. and Yuan X. L., “Research on the Test Method of Clock Deviation Measurement of Process Layer Equipment in Smart Substation”, Electric Engineering, vol. 1, pp. 150-152+159, 2020. https://link.cnki.net/doi/10.19768/j.cnki.dgjs.2020.01.053##[9] Huang J. Y., “Design and Research of Data Communication System Based on Exchange Chip MAC and PHY”, Telecom Power Technology, vol. 37, no.6, pp. 12-14, Mar. 2020. https://link.cnki.net/doi/10.19399/j.cnki.tpt.2020.06.005##[10] Huang G. P.; Xu J. Y.; Chen J. R., Liu X. F. and Liu Q. Y., “Design and engineering application of a remote intelligent test system for relay protection”, Power System Protection and Control, vol. 51, no.14, pp. 152-159, 2023. https://link.cnki.net/doi/10.19783/j.cnki.pspc.221757##[11] Liu H. L. and Ruan Y. J., “The Transient Characteristics of Fourier Algorithm and Action Time of Microcomputer Protection”, Mechanical and Electrical Equipment, vol. 37, no.6, pp. 75-78, Nov. 2020. https://link.cnki.net/doi/10.16443/j.cnki.31-1420.2020.06.016##[12] Emine E. Y. and Ramazan Y., "FPGA Based Hardware Accelerator for Euler Equations with Finite Volume Method." AIAA SCITECH 2024 Forum, AIAA 2024-0044. https://arc.aiaa.org/doi/10.2514/6.2024-0044##[13] Chen G. J., Jia X. D., Zhu R., Li M. and Li Z., “A High Precision Time Interval Measurement Method Based on Amplitude-Phase Correction”, Journal of Geomatics Science and Technology, vol. 37, no.4, pp. 340-343+349, Aug. 2020.##[14] Ranjan G. and Vatsala P., “Field Programmable Gate Array (FPGA) Based Digital Twin of Discrete Dynamic System”, AIAA SCITECH 2022 Forum, AIAA 2022-0721. https://arc.aiaa.org/doi/10.2514/6.2022-0721##[15] Wang J. K., Wu J. H., Wang W. D., Zhao J. Y., Wang T. T. and Luo W. “A new relay protection test system based on 5G and image recognition technology”, Zhejiang Electric Power, vol. 41, no. 7, pp. 42-48, 2022. https://doi.org/10.19585/j.zjdl.202207006##[16] Sezer M. and Ramazan Y., “Towards FPGA Based Digital Twin of UAV Swarms: An Area Efficient Hardware Accelerator of Transformation Matrix of 6-DoF Block”, AIAA SCITECH 2023 Forum, AIAA 2023-2130. https://doi.org/10.2514/6.2023-2130##[17] Cai D. D., He Z. M,, Liu Z. Y., Fan Z. and Wu W. J., “Calibration of FPGA carry chain delay based on code density method”, Journal of Time and Frequency, 2019, 42(03): 240-247. https://link.cnki.net/doi/10.13875/j.issn.1674-0637.2019-03-0240-08##[18] Liu P., Xu L., Liu D. C., Li H. T. and Lu W., “Research on interpolation filter algorithm of merging unit”, Electronic Design Engineering, vol. 20, no. 6, pp. 56-60, June 2019.##[19] Zhang J., Yuan L., Liu Y. L., Huang J. J. and Carlos U. L., “Editorial: Electromagnetic compatibility design and power electronics technologies in modern power systems”, Frontiers in Electronics, vol. 5, Issue, 2024.##[20] Peng J. Y., “Electric power system automation equipment electromagnetic compatibility technology”, Communication Power Supply Technology, 2019,36(02):36-37. https://link.cnki.net/doi/10.19399/j.cnki.tpt.2019.02.014##[21] Li Z. H., Shen J. H., Li Z. X., Tong Y., Wu L., “Research on an arc model of a disconnector for conduction interference of a Rogowski coil electronic transformer”, Power System Protection and Control, 2020, 48(16): 131-139. https://link.cnki.net/doi/10.19783/j.cnki.pspc.191100## ##

Feasibility Analysis of Indoor 3D Localization System with UWB Using Least Squares Trilateration 2 2 Accurate 3D Localization is very important for a wide range of applications, such as indoor navigation, industrial robotics, and motion tracking. This research focuses on indoor 3D positioning systems using ultra-wideband (UWB) devices.  Two localization experiments were conducted using the Least Squares Trilateration method. In the first experiment, anchors were at the same height, while in the second, they were at varying heights. The lowest percentage errors in the first experiment were 0% at the x-axis, 0.21% at the y-axis, and 19.75% at the z-axis. In the second experiment, the lowest percentage errors in the experiment were 1.98% at the x-axis, 0.68% at the y-axis, and 17.86% at the z-axis, demonstrating improved accuracy with varied anchor heights at the axis. This work shows the z-axis measurements are unreliable and noisy due to the limited intersection of signal waves of each anchor in a same height anchors setup. 203 213 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/242024/12/252024/12/252024/12/262024/12/262024/12/26 1403/10/6 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/202025/02/222025/02/222025/02/222025/02/222025/02/22 1403/12/4 Muhammad Naqib Mohd Shukri Muhammad Naqib Mohd Shukri Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Kampus Alam Pauh Putra 02600 Arau Perlis, Malaysia. Malaysia muhamadnaqib@studentmail.unimap.edu.my Syed Muhammad Mamduh Syed Zakaria Syed Muhammad Mamduh Syed Zakaria Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Kampus Alam Pauh Putra 02600 Arau Perlis, Malaysia. Malaysia smmamduh@unimap.edu.my Ahmad Shakaff Ali Yeon Ahmad Shakaff Ali Yeon Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Kampus Alam Pauh Putra 02600 Arau Perlis, Malaysia. Malaysia ahmadshakaff@unimap.edu.my Ammar Zakaria Ammar Zakaria Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Kampus Alam Pauh Putra 02600 Arau Perlis, Malaysia. Malaysia ammarzakaria@unimap.edu.my Latifah Munirah Kamarudin Latifah Munirah Kamarudin Faculty of Electronic Engineering & Technology, University Malaysia Perlis (UniMAP), Campus Alam Pauh Putra 02600 Arau Perlis Malaysia latifahmunirah@unimap.edu.my 3D Localization System Ultra-Wideband Trilateration. [1] H. Pan, X. Qi, M. Liu, and L. Liu, “Indoor scenario-based UWB anchor placement optimization method for indoor localization,” Expert Systems With Applications, vol. 205, p. 117723, Nov. 2022, doi: 10.1016/j.eswa.2022.117723.##[2] Y. Han, X. Zhang, Z. Lai, and Y. Geng, “TOF-Based Fast Self-Positioning Algorithm for UWB Mobile Base Stations,” Sensors, vol. 21, no. 19, p. 6359, Sep. 2021, doi: 10.3390/s21196359.##[3] R. Mazraani, M. Saez, L. Govoni, and D. Knobloch, “Experimental results of a combined TDOA/TOF technique for UWB based localization systems,” May 2017, doi: 10.1109/iccw.2017.7962796.##[4] C. Li et al., “CRLB-based Positioning Performance of Indoor Hybrid AoA/RSS/ToF Localization,” Sep. 2019, doi: 10.1109/ipin.2019.8911771. ##[5] Geok, T. K., Aung, K. Z., Aung, M. S., Soe, M. T., Abdaziz, A., Liew, C. P., Hossain, F., Tso, C. P. and Yong, W. H., Review of Indoor Positioning: Radio Wave Technology, Applied Sciences, vol. 11, no. 1, p. 279, from https://doi.org/10.3390/app11010279, December 30, 2020. DOI: 10.3390/app11010279 ##[6] Ferrigno, L., Miele, G., Milano, F., Pingerna, V., Cerro, G. and Laracca, M., A UWB-Based Localization System: Analysis of the Effect of Anchor Positions and Robustness Enhancement in Indoor Environments, from https://doi.org/10.1109/i2mtc50364.2021.9459845, May 17, 2021. DOI: 10.1109/i2mtc50364.2021.9459845 ##[7] Madfolio, Least-Squares-Trilateration/README.Md at Master · Madfolio/Least-Squares-Trilateration, GitHub, n.d.##[8] Airtls, AIRTLS Sport | Precise Real-Time Locating Combined with Innovative Analyses, n.d.##[9] Yasukawa, Y., Higashi, Y., Masuda, A. and Miura, N., Automatic Anchor Caliburation for UWB-Based Indoor Positioning Systems, from https://doi.org/10.1109/tencon50793.2020.9293741, November 16, 2020. DOI: 10.1109/tencon50793.2020.9293741##[10] Alrajeh, N. A., Bashir, M. and Shams, B., Localization Techniques in Wireless Sensor Networks, International Journal of Distributed Sensor Networks, vol. 9, no. 6, p. 304628, from https://doi.org/10.1155/2013/304628, June 1, 2013. DOI: 10.1155/2013/304628##[11] Win, M. Z., Buehrer, R. M., Chrisikos, G., Conti, A. and Poor, H. V., Foundations and Trends in Localization Technologies — Part I [Scanning the Issue], Proceedings of the IEEE, vol. 106, no. 6, pp. 1019–21, from https://doi.org/10.1109/jproc.2018.2837342, June 1, 2018. DOI: 10.1109/jproc.2018.2837342##[12] Chen, H. and Dhekne, A., Spoofing Evident and Spoofing Deterrent Localization Using Ultra-Wideband (UWB) Active-Passive Ranging, IEEE Journal of Indoor and Seamless Positioning and Navigation, vol. 2, pp. 12–24, from https://doi.org/10.1109/jispin.2023.3343336, January 1, 2024. 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Z., Localization of Multiple Sources Using Time-Difference of Arrival Measurements, from https://doi.org/10.1109/icassp.2017.7952737, March 1, 2017. DOI: 10.1109/icassp.2017.7952737##[17] Xiong, H., Chen, Z., An, W. and Yang, B., Robust TDOA Localization Algorithm for Asynchronous Wireless Sensor Networks, International Journal of Distributed Sensor Networks, vol. 11, no. 5, p. 598747, from https://doi.org/10.1155/2015/598747, May 1, 2015. DOI: 10.1155/2015/598747##[18] Mazraani, R., Saez, M., Govoni, L. and Knobloch, D., Experimental Results of a Combined TDOA/TOF Technique for UWB Based Localization Systems, from https://doi.org/10.1109/iccw.2017.7962796, May 1, 2017. DOI: 10.1109/iccw.2017.7962796##[19] Park, J. and Ko, Y.-B., PedLoc: UWB-Based Pedestrian Localization for Autonomous Vehicles, Internet of Things, vol. 26, p. 101194, from https://doi.org/10.1016/j.iot.2024.101194, July 1, 2024. 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Segmented Bending Soft Actuator for Enhanced Finger Flexion in Rehabilitation Glove 2 2 Rehabilitation devices like assistive gloves require bending-type soft actuators for controlled, repetitive finger movements essential for therapy. However, non-segmented actuators often struggle to replicate natural finger articulation, which can cause discomfort and reduce patient compliance. This paper presents the design and assembly of a segmented bending pneumatic soft actuator to achieve index finger flexion, aiming to improve comfort and support natural finger movement at low pressure. The actuator is integrated into a glove with a flexible bend sensor to measure the flexion angle of the metacarpophalangeal joint. Ecoflex 0-50 A-B silicone rubber is used in the fabrication, with air bubbles removed to ensure consistent actuator performance. The study investigates the actuator's performance and the sensor's ability to accurately measure joint flexion. The results, presented through detailed graphs, analyze the actuator’s flexibility, bending, and elongation under different pressure scenarios, offering insights into its effectiveness in improving patient comfort, joint articulation, and rehabilitation outcomes. 214 222 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/242024/12/252024/12/252024/12/262024/12/262024/12/262024/12/26 1403/10/6 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/202025/02/222025/02/222025/02/222025/02/222025/02/222025/02/22 1403/12/4 Nurul Hidayah Rodzuan Nurul Hidayah Rodzuan Department of Electrical Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, 84600 Pagoh, Johor, Malaysia Malaysia nurulhidayahrodzuan@gmail.com Ili Najaa Aimi Mohd Nordin Ili Najaa Aimi Mohd Nordin Department of Electrical Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, 84600 Pagoh, Johor, Malaysia Malaysia ilinajaa@uthm.edu.my Ahmad ‘Athif Mohd Faudzi Ahmad ‘Athif Mohd Faudzi Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, 51400 Kuala Lumpur, Malaysia Malaysia athif@utm.my Noraishikin Zulkarnain Noraishikin Zulkarnain Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia Malaysia shikinzulkarnain@ukm.edu.my Muhammad Rusydi Muhammad Razif Muhammad Rusydi Muhammad Razif Department of Electrical Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, 84600 Pagoh, Johor, Malaysia Malaysia rusydi@uthm.edu.my Nik Normunira Mat Hassan Nik Normunira Mat Hassan Department of Mechanical Engineering Technology, Faculty of Engineering Technology, Universiti Tun Hussein Onn Malaysia, 84600 Pagoh, Johor, Malaysia Malaysia normunira@uthm.edu.my Muhamad Hazwan Abdul Hafidz Muhamad Hazwan Abdul Hafidz A2Tech Sdn. Bhd., Level 2, Block B, V01, Faculty of Engineering, UTM, 81310 Skudai, Johor, Malaysia Malaysia hazwan@a2tech.my Assistive Glove Finger flexion Flexible Bend Sensor Segmented Bending Actuator [1] N. R. Nik Nasihah, A. Asokan, D. Mayakrishnan, and H. Annamalai, "Exploring Stroke Rehabilitation in Malaysia: Are Robots Better than Humans for Stroke Recuperation?" Malaysian Journal of Medical Sciences, Vol. 28, No. 4, pp. 14–23, Aug. 2021. https://doi.org/10.21315/mjms2021.28.4.3 ##[2] F. Li, J. Chen, G. Ye, S. Dong, Z. Gao, and Y. Zhou, "Soft Robotic Glove with Sensing and Force Feedback for Rehabilitation in Virtual Reality," Biomimetics, Vol. 8 (1), No. 83, pp. 1-15, Feb. 2023. https://doi.org/10.3390/biomimetics8010083##[3] Y. Hao and Y. Visell, "Beyond Soft Hands: Efficient Grasping with Non-Anthropomorphic Soft Grippers," Frontiers in Robotics and AI, Vol. 8, No. 632006, pp. 1-8, Jul. 2021. https://doi.org/10.3389/frobt.2021.632006##[4] C. Y. Chu and R. M. Patterson, "Soft Robotic Devices for Hand Rehabilitation and Assistance: A Narrative Review," Journal of NeuroEngineering and Rehabilitation, Vol. 15, No. 9, pp. 1-14, Feb. 2018. https://doi.org/10.1186/s12984-018-0350-6##[5] D. H. Kim, Y. Lee, and H.-S. Park, "Bioinspired High-Degrees of Freedom Soft Robotic Glove for Restoring Versatile and Comfortable Manipulation," Soft Robotics, Vol. 9, No. 4, pp. 734-744, Aug. 2022. https://doi.org/10.1089/soro.2020.0167##[6] M. Li, T. Wang, Y. Zhuo, B. He, T. Tao, J. Xie, and G. Xu "A Soft Robotic Glove for Hand Rehabilitation Training Controlled by Movements of the Healthy Hand," in Proc. 17th Int. Conf. Ubiquitous Robots (UR), Jun Kyoto, Japan, 2020. https://doi.org/10.1109/ur49135.2020.9144753##[7] W. Thimabut, P. Terachinda, and W. Kitisomprayoonkul, "Effectiveness of a Soft Robotic Glove to Assist Hand Function in Stroke Patients: A Cross-Sectional Pilot Study," Rehabilitation Research and Practice, Vol. 2022, No. 738219, pp. 1-8, Apr. 2022. https://doi.org/10.1155/2022/3738219##[8] X. Chen, L. Gong, L. Wei, S. -C. Yeh, L. Da Xu, L. Zheng and Z. Zou, "A Wearable Hand Rehabilitation System with Soft Gloves," IEEE Transactions on Industrial Informatics, Vol. 17, No. 2, pp. 943–952, Feb. 2021. https://doi.org/10.1109/tii.2020.3010369##[9] K. Nuckols, W. Moyo, C. Hohimer, C. Glover, D. Wagner, A. Cloutier, D. Lin, and C. Walsh, "Proof of Concept of Soft Robotic Glove for Hand Rehabilitation in Stroke Survivors," Archives of Physical Medicine and Rehabilitation, Vol. 100, No. 12, pp. e195, Dec. 2019. https://doi.org/10.1016/j.apmr.2019.10.099##[10] P. Polygerinos, Z. Wang, K. C. Galloway, R. J. Wood, and C. J. Walsh, "Soft Robotic Glove for Combined Assistance and At-Home Rehabilitation," Robotics and Autonomous Systems, Vol. 73, No. November 2015, pp. 135–143, Nov. 2015. https://doi.org/10.1016/j.robot.2014.08.014##[11] N. Gao, P. Chen, and L. Liang, "BCI–VR-Based Hand Soft Rehabilitation System with Its Applications in Hand Rehabilitation after Stroke," Int. J. Precision Eng. Manuf., Vol. 24, No. 8, pp. 1403–1424, June 2023. https://doi.org/10.1007/s12541-023-00835-2##[12] S. N. S. Selamat, R. C. Me, H. A. Ainuddin, M. S. F. Salim, H. R. Ramli, and M. H. Romli, "The Application of Technological Intervention for Stroke Rehabilitation in Southeast Asia: A Scoping Review with Stakeholders’ Consultation," Frontiers in Public Health, Vol. 9, No. 783565, pp. 1-12, Feb. 2022. https://doi.org/10.3389/fpubh.2021.783565##[13] I. N. A. M. Nordin, M. R. M. Razif, A. A. M. Faudzi, E. Natarajan, K. Iwata, and K. Suzumori, "3-D Finite-Element Analysis of Fiber-Reinforced Soft Bending Actuator for Finger Flexion," in Proc. IEEE/ASME Int. Conf. Adv. Intell. Mechatronics (AIM), Wollongong, NSW, Australia, 2013. https://doi.org/10.1109/aim.2013.6584080##[14] I. N. A. M. Nordin, A. A. M. Faudzi, M. Z. Kamarudin, D. E. O. Dewi, T. Rehman, and M. R. M. Razif, "Grip Force Measurement of Soft-Actuated Finger Exoskeleton," Jurnal Teknologi, Vol. 78, No. 6–13, pp. 25-30, June 2016. https://doi.org/10.11113/jt.v78.9268##[15] F. Yang, Q. Ruan, Y. Man, Z. Xie, H. Yue, B. Li and R. Liu "Design and Optimize of a Novel Segmented Soft Pneumatic Actuator," IEEE Access, Vol. 8, pp. 122304–122313, July 2020. https://doi.org/10.1109/ACCESS.2020.3006865##[16] G. Belforte, G. Eula, A. Ivanov, and S. Sirolli, "Soft Pneumatic Actuators for Rehabilitation," Actuators, Vol. 3, No. 2, pp. 84–106, May 2014. https://doi.org/10.3390/act3020084##[17] B. Wang, A. McDaid, M. Biglari-Abhari, and K. C. Aw, "Design and Development of a Glove for Post-Stroke Hand Rehabilitation," in Proc. IEEE Int. Conf. Adv. Intell. Mechatronics (AIM), Munich, Germany, 2017. https://doi.org/10.1109/aim.2017.8014157##[18] K. Nuckols, C. J. Hohimer, C. Glover, D. S. de Lucena, W. Moyo, D. Wagner, A. Cloutier, D. J. Lin, and C. J. Walsh, "Effects of a Soft Robotic Glove Using a High Repetition Protocol in Chronic Stroke: A Pilot Study," in Proc. 8th IEEE RAS/EMBS Int. Conf. Biomed. Robot. Biomechatronics (BioRob), New York, USA 2020. https://doi.org/10.1109/biorob49111.2020.9224291##[19] J. Wang, Y. Fei, and W. Pang, "Design, Modeling, and Testing of a Soft Pneumatic Glove with Segmented PneuNets Bending Actuators," IEEE/ASME Transactions on Mechatronics,Vol. 24, No. 3, pp. 990-1001, June 2019. https://doi.org/10.1109/TMECH.2019.2911992## ##

Design and Performance Analysis of Fuzzy Logic Controller for Solar Photovoltaic System 2 2 This study presents a Fuzzy Logic Controller (FLC)-based Maximum Power Point Tracking (MPPT) system for solar Photovoltaic (PV) setups, integrating PV panels, a boost converter, and battery storage. While FLC is known for its robustness in PV systems, challenges in battery charging and discharging efficiency can affect performance. The research addresses these challenges by optimizing battery charging, preventing overcharging, and enhancing overall system efficiency. The FLC MPPT system is designed to regulate the battery's State of Charge (SOC) while evaluating system performance under varying solar irradiance and temperature conditions. The system is modeled and simulated using MATLAB/Simulink, incorporating the PV system, MPPT algorithm, and models for the PV module and boost converter. System efficiency is assessed under different scenarios, with results showing 97.92% efficiency under Standard Test Conditions (STC) at 1000 W/m² and 25°C. Additionally, mean efficiencies of 97.13% and 96.13% are observed under varying irradiance and temperature, demonstrating the effectiveness of the FLC MPPT in regulating output. The system also extends battery life by optimizing power transfer between the PV module, boost converter, and battery, ensuring regulated SOC. 223 232 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/242024/12/252024/12/252024/12/262024/12/262024/12/262024/12/262024/12/30 1403/10/10 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/202025/02/222025/02/222025/02/222025/02/222025/02/222025/02/222025/02/22 1403/12/4 Kumuthawathe Ananda-Rao Kumuthawathe Ananda-Rao Faculty of Electrical Engineering & Technology, University Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia kumuthawathe@unimap.edu.my Steven Taniselass Steven Taniselass Faculty of Electronic Engineering & Technology, University Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia steven@unimap.edu.my Afifah Shuhada Rosmi Afifah Shuhada Rosmi Faculty of Electrical Engineering & Technology, University Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia afifahshuhada@unimap.edu.my Aimi Salihah Abdul Nasir Aimi Salihah Abdul Nasir Faculty of Electrical Engineering & Technology, University Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia aimisalihah@unimap.edu.my Nor Hanisah Baharudin Nor Hanisah Baharudin Faculty of Electrical Engineering & Technology, University Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia norhanisah@unimap.edu.my Indra Nisja Indra Nisja Department of Electrical Engineering, Faculty of Industrial Technology, Bung Hatta University, Padang, West Sumatera, Indonesia. Indonesia drindra765@bunghatta.ac.id Battery Fuzzy Logic Controller (FLC) MATLAB/Simulink Maximum Power Point Tracking (MPPT) Solar Photovoltaic (PV). [1] N. Sulaiman, S. I. Ihsan, S. N. S. A. Bakar, Z. A. A. Majid, and Z. A. Zakaria, "Evacuated Tubes Solar Air Collectors: A Review on Design Configurations, Simulation Works and Applications," Progress in Energy and Environment, vol. 25, pp. 10–32, 2023.##[2] Z. Ilham, N. A. I. Saad, W. A. A. Q. I. Wan, and A. A. Jamaludin, "Multi-Criteria Decision Analysis for Evaluation of Potential Renewable Energy Resources in Malaysia," Progress in Energy and Environment, vol. 21, 2022.##[3] M. Ali, M. Ahmad, M. A. Koondhar, M. S. Akram, A. Verma, and B. Khan, “Maximum Power Point Tracking for Grid-Connected Photovoltaic System Using Adaptive Fuzzy Logic Controller,” Computers and Electrical Engineering, vol. 110, 2023.##[4] M. Zerouali, A. El Ougli, and B. Tidhaf, “A Robust Fuzzy Logic PI Controller for Solar System Battery Charging,” Int. J. Power Electronics and Drive Syst., vol. 14, pp. 384–394, 2023.##[5] S. K. Manas and B. Bhushan, “Performance Analysis of Fuzzy Logic-Based MPPT Controller for Solar PV System Using Quadratic Boost Converter,” Advances in Energy Technology, vol. 766, no. LNEE, 28, pp. 69–79, 2021.##[6] T. M. Lima and J. A. C. B. Oliveira, "FPGA-Based Fuzzy Logic Controllers Applied to the MPPT of PV Panels – A Systematic Review," IEEE Trans. Fuzzy Syst., pp. 1–10, 2024.##[7] A. Daraz, B. A. Basit, and G. Zhang, “Performance Analysis of PID Controller and Fuzzy Logic Controller for DC-DC Boost Converter,” PLoS One, vol. 18, no. 10, pp. e0281122, 2023.##[8] T. Hai, J. Zhou, and K. Muranaka, "An Efficient Fuzzy-Logic Based MPPT Controller for Grid-Connected PV Systems by Farmland Fertility Optimization Algorithm," Optik, vol. 267, p. 169636, 2022.##[9] A. Mutia, D. Abdullah, K. Kraugusteeliana, S. A. Pramono, and H. Sama, “Simulation of Solar Panel Maximum Power Point Tracking Using the Fuzzy Logic Control Method,” Majlesi J. Electr. Eng., vol. 17, no. 2, pp. 29–39, 2023.##[10] C. Pavithra, V. S. Vidhyareni, M. Vijayadharshini, S. A. K. B. Shree, and N. Varsha, “Comparison of Solar P&O and FLC-Based MPPT Controllers & Analysis Under Dynamic Conditions,” EAI Endorsed Trans. Energy Web, vol. 11, 2024.##[11] W. Hayder, A. Abid, M. B. Hamed, E. Ogliari, and L. Sbita, "Comparison of MPPT Methods FLC & PSO for PV System Under Variable Irradiance and Temperature," in Proc. 18th Int. Multi-Conf. Systems, Signals & Devices (SSD), Monastir, Tunisia, pp. 1247–1251, 2021.##[12] N. Siddiqui and M. S. Ghole, “Comparative Analysis of P&O and Fuzzy Logic Based MPPT Methods for Bi-Facial Photovoltaic Module,” in Proc. 2024 IEEE Int. Students' Conf. Electrical, Electronics and Computer Sci., 2024.##[13] M. H. Azmi, S. M. Noor, and S. Musa, “Fuzzy Logic Control Based Maximum Power Point Tracking Technique in Standalone Photovoltaic System,” Int. J. Power Electronics and Drive Syst., vol. 14, pp. 1110–1120, 2023.##[14] S. Kumar and B. K. Balakrishna, “A Novel Design and Analysis of Hybrid Fuzzy Logic MPPT Controller for Solar PV System Under Partial Shading Conditions,” Sci. Rep., vol. 14, 2024.##[15] S. Abu, A. Nadzirah, A. Al-Shetwi, M. A. Hannan, P. J. Ker, M. A. Rahman, and K. Muttaqi, “Fuzzy Based BSA Optimization for Maximum Power Point Tracking Controller Performance Evaluation,” IEEE Ind. Appl. Soc. Annu. Meet. (IAS), Vancouver, BC, Canada, pp. 1–8, 2021.##[16] M. Haseeb, A. Mansour, and E. Othman, “Enhancing of Single-Stage Grid-Connected Photovoltaic System Using Fuzzy Logic Controller,” Int. J. Electr. Comput. Eng. (IJECE), vol. 14, pp. 2400–2412, 2024.##[17] K. Bouguerra, S. Latreche, and M. Khemliche, "Comparative Study Between IncCond and FLC and SMC Algorithms for MPPT Control for Grid Connected PV System," in Proc. 2nd Int. Conf. Electr. Eng. Automatic Control (ICEEAC), Setif, Algeria, 2024.##[18] P. S. Acharya and P. S. Aithal, "A Comparative Study of MPPT and PWM Solar Charge Controllers and Their Integrated System," J. Phys. Conf. Ser., vol. 1712, no. 1, p. 012023, 2020.##[19] B. Chandrashekar, J. Pradeep, M. M., M. Sivasubramanian, K. L. Khandan, and J. A. Dhanraj, "A Fuzzy Logic Controller for a Photovoltaic System Relying on a Closed-Loop DC-DC Converter," in Proc. 9th Int. Conf. Sci. Technol. Eng. Math. (ICONSTEM), Chennai, India, pp. 1–5, 2024.##[20] K. Ananda-Rao et al., “MPPT Charge Controller Using Fuzzy Logic for Battery Integrated with Solar Photovoltaic System,” J. Adv. Res. Appl. Sci. Eng. Technol., vol. 47, no. 2, pp. 171–182, 2024.##[21] S. Kaur and S. Vig, "Modeling of MPPT-Based Solar Eco-System Using Fuzzy Logic Controller," in IOP Conf. Ser.: Earth Environ. Sci., vol. 1110, no. 1, p. 012079, 2023.##[22] L. K. Narwat and J. Dhillon, "Design and Operation of Fuzzy Logic Based MPPT Controller Under Uncertain Condition," J. Phys. Conf. Ser., vol. 1854, no. 1, p. 012035, 2021.##[23] M. Bouksaim, M. Mekhfioui, and M. N. Srifi, “Design and Implementation of Modified INC, Conventional INC, and Fuzzy Logic Controllers Applied to a PV System Under Variable Weather Conditions,” Designs, vol. 5, no. 4, p. 71, 2021.##[24] R. Sharma, V. Saran, S. Kanaujia, and S. Gupta, “Evaluation of MPPT Controller Performance Using Fuzzy Logic Design with Wireless Sensor Node,” in Proc. Int. Conf. Smart Syst. App. Electr. Sci. (ICSSES), 2024.## ##

Analysis of Partial Discharge Measurements using Coupling Capacitor in Rotating Machine 2 2 Partial discharge (PD) is a significant concern in the operation of rotating machines such as generators and motors, as it can lead to insulation degradation over time, reducing the reliability and lifespan of the machines. To monitor PD activity, coupling capacitors (CC) are widely used as sensors for online PD detection, as they can effectively capture PD pulses in high-voltage (HV) rotating machines. The primary objective of this research is to measure and analyze PD signals using a CC sensor for HV rotating machines under varying input voltages and frequencies, following the guidelines of the IEC 60270 standard and utilizing the MPD 600 device. The experimental setup includes performing insulation resistance (IR) testing, PD calibration, and PD measurement. Additionally, this paper provides a detailed study of PD signal characteristics, specifically focusing on phase-resolved partial discharge (PRPD) patterns, to understand the behavior of PD in HV rotating machines, enhancing fault diagnosis and preventive maintenance strategies. 233 249 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/242024/12/252024/12/252024/12/262024/12/262024/12/262024/12/262024/12/302024/12/31 1403/10/11 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/202025/02/222025/02/222025/02/222025/02/222025/02/222025/02/222025/02/222025/03/06 1403/12/16 Ahmad Syukri Abd Rahman Ahmad Syukri Abd Rahman MHH Condition Monitoring Sdn Bhd, No.8, Tingkat 1, Jalan Sumazau 1J/KU5, Bandar Bukit Raja, 41050 Klang, Selangor, Malaysia. Malaysia ahmadsyukri@studentmail.unimap.edu.my Mohamad Nur Khairul Hafizi Rohani Mohamad Nur Khairul Hafizi Rohani Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia khairulhafizi@unimap.edu.my Nur Dini Athirah Gazata Nur Dini Athirah Gazata Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia Afifah Shuhada Rosmi Afifah Shuhada Rosmi Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia afifahshuhada@unimap.edu.my Ayob Nazmi Nanyan Ayob Nazmi Nanyan Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia ayobnazmy@unimap.edu.my Aiman Ismail Mohamed Jamil Aiman Ismail Mohamed Jamil Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia aimanismail@studentmail.unimap.edu.my Mohd Helmy Halim Abdul Majid Mohd Helmy Halim Abdul Majid MHH Condition Monitoring Sdn Bhd, No.8, Tingkat 1, Jalan Sumazau 1J/KU5, Bandar Bukit Raja, 41050 Klang, Selangor, Malaysia. Malaysia helmy.uitm@mhhlegacy.com.my Normiza Masturina Samsuddin Normiza Masturina Samsuddin MHH Condition Monitoring Sdn Bhd, No.8, Tingkat 1, Jalan Sumazau 1J/KU5, Bandar Bukit Raja, 41050 Klang, Selangor, Malaysia. Malaysia normiza@mhhlegacy.com.my Partial Discharge (PD) Coupling Capacitor (CC) High Voltage Rotating Machine (HVRM) Phase Resolve Partial Discharge (PRPD). [1] G. C. Stone, H. G. Sedding, C. Chan, and C. Wendel, "Comparison of Low Frequency and High Frequency PD Measurements on Rotating Machine Stator Windings," 2018 IEEE Electr. Insul. Conf. EIC 2018, no. June, pp. 349-352, 2018, doi: 10.1109/EIC.2018.8481128.##[2] F. Oettl, C. Engelen, and C. Staubach, "Localization of PD-events in HV-Windings of Rotating Machines," Diagnostika 2022 - 2022 Int. Conf. Diagnostics Electr. Eng. Proc., no. Figure 2, pp. 1-4, 2022, doi: 10.1109/Diagnostika55131.2022.9905130.##[3] A. Z. Bin Abdullah et al., "Wavelet based de-noising for on-site partial discharge measurement signal," Indones. J. Electr. Eng. Comput. Sci., vol. 16, no. 1, pp. 259-266, 2019, doi: 10.11591/ijeecs.v16.i1.pp259-266.##[4] H. Chai, B. T. Phung, and S. Mitchell, "Application of UHF sensors in power system equipment for partial discharge detection: A review," Sensors (Switzerland), vol. 19, no. 5, 2019, doi: 10.3390/s19051029.##[5] C. L. Wooi, Z. Abul-Malek, M. N. K. Hafizi Rohani, A. M. Bin Yusof, S. N. M. Arshad, and A. I. Elgayar, "Comparison of lightning return stroke channel-base current models with measured lightning current," Bull. Electr. Eng. Informatics, vol. 8, no. 4, pp. 1478-1488, 2019, doi: 10.11591/eei.v8i4.1613.##[6] C. C. Yii, M. N. K. H. Rohani, M. Isa, S. I. S. Hassan, B. Ismail, and N. Hussin, "Multi-end partial discharge location algorithm based on trimmed mean data filtering technique for MV underground cable," 2015 IEEE Student Conf. Res. Dev. SCOReD 2015, pp. 345-350, 2015, doi: 10.1109/SCORED.2015.7449353.##[7] Y. Yamanaka, T. Umemoto, R. Ikeda, N. Okajima, T. Sakurai, and T. Okamoto, "Deterioration of Corona Armor Tape by Partial Discharge and its Lifetime Evaluation for Form-wound Rotating Machine," 2023 IEEE Electr. Insul. Conf. EIC 2023, pp. 1-4, 2023, doi: 10.1109/EIC55835.2023.10177341.##[8] S. H. K. Hamadi et al., "Modelling of partial discharge signal and noise interference using labview," IEEE Student Conf. Res. Dev. Inspiring Technol. Humanit. SCOReD 2017 - Proc., vol. 2018-Janua, pp. 451-455, 2017, doi: 10.1109/SCORED.2017.8305441.##[9] C. P. Malliou, A. Karlis, and M. G. Danikas, "Electrical machine insulation: Partial discharges, consequences and diagnostic technique," Proc. 2017 IEEE 11th Int. Symp. Diagnostics Electr. Mach. Power Electron. Drives, SDEMPED 2017, vol. 2017-Janua, pp. 468-474, 2017, doi: 10.1109/DEMPED.2017.8062396.##[10] M. Chiampi, G. Crotti, Y. Hu, and A. Sardi, "Calibration of partial discharge measuring systems by a reference impulse charge generator," 16th IMEKO TC4 Int. Symp., pp. 155-160, 2008.##[11] C. Zachariades, R. Shuttleworth, R. Giussani, and R. Mackinlay, "Optimization of a high-frequency current transformer sensor for partial discharge detection using finite-element analysis," IEEE Sens. J., vol. 16, no. 20, pp. 7526-7533, 2016, doi: 10.1109/JSEN.2016.2600272.##[12] S. Chaudhuri, S. Ghosh, D. Dey, S. Munshi, B. Chatterjee, and S. Dalai, "Denoising of partial discharge signal using a hybrid framework of total variation denoising-autoencoder," Meas. J. Int. Meas. Confed., vol. 223, no. September, p. 113674, 2023, doi: 10.1016/j.measurement.2023.113674.##[13] H. M. B. Sibanyoni, J. J. Walker, and J. S. Djeumen, "Evaluation of the behaviour of HFCTs for corona measurement under HVDC application," 2020 Int. SAUPEC/RobMech/PRASA Conf. SAUPEC/RobMech/PRASA 2020, pp. 1-3, 2020, doi: 10.1109/SAUPEC/RobMech/PRASA48453.2020.9041012.##[14] M. A. Kashiha, D. Z. Tootaghaj, and D. Djamshidi, "Partial discharge source classification and denoising in rotating machines using discrete wavelet transform and directional coupling capacitors," Transm. Distrib. Conf. Expo. Asia Pacific, T D Asia 2009, pp. 1-4, 2009, doi: 10.1109/TD-ASIA.2009.5356903.##[15] S. Mohammad Hassan Hosseini and S. Mahdi Mazlomi, "Diagnosing and Online Partial Discharge Location with Using Coupling Capacitor in Induction Motors," GMSARN Int. J., vol. 17, pp. 319-328, 2023.##[16] M. Fritsch and M. Wolter, "High-Frequency Current Transformer Design and Construction Guide," IEEE Trans. Instrum. Meas., vol. 71, pp. 1-9, 2022, doi: 10.1109/TIM.2022.3177189.##[17] Suwarno, "Partial discharge in high voltage insulating materials," Int. J. Electr. Eng. Informatics, vol. 8, no. 1, pp. 147-163, 2016, doi: 10.15676/ijeei.2016.8.1.11.##[18] Z. Faizol et al., "Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review," Appl. Sci., vol. 13, no. 17, 2023, doi: 10.3390/app13179605.##[19] M. M. Yaacob et al., "Review on partial discharge detection techniques related to high voltage power equipment using different sensors," Photonic Sensors, vol. 4, no. 4, pp. 325-337, 2014, doi: 10.1007/s13320-014-0146-7.##[20] M. M. Yaacob, M. A. Alsaedi, J. R. Rashed, A. M. Dakhil, and S. F. Atyah, "Review on partial discharge detection techniques related to high voltage power equipment using different sensors," Photonic Sensors, vol. 4, no. 4, pp. 325-337, 2014, doi: 10.1007/s13320-014-0146-7.## ##

Electric Field and Space Charge Distribution in Propylene Carbonate Under Continuous DC Electric Field using Kerr Effect 2 2 The study investigates the electric field and space charge distributions in propylene carbonate under direct current (DC) applied fields using Kerr effect. Propylene carbonate is known for its high permittivity and is utilised in many applications, including electrochemical systems and dielectric materials. Understanding the behaviour of electric fields and space charge distributions within propylene carbonate is critical for optimising its performance in these applications. In the study, Kerr effect is employed which by applying the DC electric field across the test liquid for measuring the electric field and space charge distributions within the propylene carbonate. The experimental setup involved a controlled application of DC fields, and the Kerr effect measurements were conducted using an optical system. The results show significant understandings into the behaviour of space charges and their influence on the electric field distribution in propylene carbonate. Distinct patterns of charge accumulation and electric field distortion were observed and analysed in the dielectric liquid properties and charge transport mechanisms. The relationship between electric fields and space charges in propylene carbonate under DC conditions has been provided by the findings. The study also shows that the Kerr effect is a useful tool for studying electric field distributions in complex materials. 250 259 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/242024/12/252024/12/252024/12/262024/12/262024/12/262024/12/262024/12/302024/12/312024/12/31 1403/10/11 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/202025/02/222025/02/222025/02/222025/02/222025/02/222025/02/222025/02/222025/03/062025/02/22 1403/12/4 Z. N. Zakaria Z. N. Zakaria Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia. Malaysia M. S. Laili M. S. Laili Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia. Malaysia N. A. Rahman N. A. Rahman Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia. Malaysia P. L. Lewin P. L. Lewin United Kingdom T. Andritsch T. Andritsch School of Electronics and Computer Science, University of Southampton, United Kingdom. United Kingdom N. Hussin N. Hussin Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Malaysia. Malaysia nuriziani@unimap.edu.my Electric field Space charge Kerr effect Propylene carbonate Dielectric liquid [1] M. Zahn, ‘Space charge effects in dielectric liquids’, in The Liquid State and Its Electrical Properties, vol. 193, Springer US, 1988, pp. 367–430.##[2] T. J. Lewis, ‘Basic electrical processes in dielectric liquids’, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 1, no. 4, pp. 630–643, 1994, doi: 10.1109/94.311706.##[3] T. J. Gung, A. Ustundag, and M. Zahn, ‘Preliminary Kerr Electro-Optic Field Mapping Measurements in Propylene Carbonate Using Point-Plane Electrodes’, J Electrostat, vol. 3886, no. 99, pp. 79–89, 1999.##[4] J. Shi, Q. Yang, W. Sima, L. Liao, S. Huang, and M. Zahn, ‘Space charge dynamics investigation based on Kerr electro-optic measurements and processing of CCD images’, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 20, no. 2, pp. 601–611, 2013, doi: 10.1109/TDEI.2013.6508764.##[5] Z. Zhang, S. Wu, W. He, and Q. Yang, “A Novel Transient Electric Field Measurement for Low Kerr Constant Liquid Dielectrics Based on Concave Spherical Mirror Conjugate Structure,” IEEE Transactions Instrumentation and Measurement, 2023, doi: 10.1109/TIM.2022.3225024.##[6] H. Ihori, S. Ninomiya, and M. Fujii, ‘Optical measurement of electric field distributions with time in propylene carbonate’, Electronics and Communications in Japan, vol. 94, no. 9, pp. 45–51, 2011, doi: 10.1002/ecj.10351.##[7] Z. N. Zakaria, T. Andritsch, and P. L. Lewin, ‘Kerr Measurement Approaches in Propylene Carbonate under DC Electric Field’, IEEE 2nd International Conference on Dielectrics, ICD 2018, pp. 1–4, 2018, doi: 10.1109/ICD.2018.8468415.##[8] Z. N. Zakaria, P. L. Lewin, and T. Andritsch, ‘Light Intensity Measurement of Kerr Effect Using Photodiode and High-Speed Camera in Propylene Carbonate under Applied DC Electric Fields’, J Phys Conf Ser, vol. 1878, no. 1, pp. 0–9, 2021, doi: 10.1088/1742-6596/1878/1/012044.##[9] A. W. Bright, B. Makin, and A. J. Pearmain, ‘Field distribution in nitrobenzene using the Kerr effect’, J Phys D Appl Phys, vol. 2, no. 3, p. 447, 1969, [Online]. Available: http://iopscience.iop.org/0022-3727/2/3/319##[10] E. C. Cassidy, R. E. Hebner, M. Zahn, and R. J. Sojka, ‘Kerr-effect studies of an insulating liquid under varied high-voltage conditions’, IEEE Transactions on Electrical Insulation, vol. EI-9, no. 2, pp. 43–56, 1974, doi: 10.1109/TEI.1974.299310.##[11] M. Zahn and T. Takada, ‘High voltage electric field and space-charge distributions in highly purified water’, J Appl Phys, vol. 54, no. 9, pp. 4762–4775, 1983, doi: 10.1063/1.332810.##[12] X. Zhang and M. Zahn, ‘Kerr electro-optic field mapping study of the effect of charge injection on the impulse breakdown strength of transformer oil’, Appl Phys Lett, vol. 103, no. 16, p. 162906, 2013, doi: 10.1063/1.4826185.##[13] K. Tanaka and T. Takada, ‘Measurement of the 2-Dimensional Electric Field Vector in Dielectric Liquids’, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 1, no. 4, pp. 747–753, 1994, doi: 10.1109/94.311720.##[14] A. Helgeson and M. Zahn, ‘Kerr electro-optic measurements of space charge effects in HV pulsed propylene carbonate’, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 9, no. 5, pp. 838–844, 2002, doi: 10.1109/TDEI.2002.1038666.##[15] W. Sima, Q. Chen, P. Sun, M. Yang, H. Guo, and L. Ye, ‘Asymmetric injection and distribution of space charges in propylene carbonate under impulse voltage’, J Phys D Appl Phys, vol. 51, no. 21, 2018, doi: 10.1088/1361-6463/aab99c.##[16] Z. Zhang, Q. Yang, S. Wu, and W. He, ‘Temperature Effect on Dielectric Properties of Propylene Carbonate under Switching Overvoltage’, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 29, no. 2, pp. 428–436, Apr. 2022, doi: 10.1109/TDEI.2022.3157934.##[17] T. Maeno, Y. Nonaka, and T. Takada, ‘Determination of Electric Field Distribution in Oil using the Kerr-effect Technique after Application of dc Voltage’, IEEE Transactions on Electrical Insulation, vol. 25, no. 3, pp. 475–480, 1990, doi: 10.1109/14.55719.##[18] X. Zhang, ‘Electro-optic signatures of turbulent electroconvection in dielectric liquids’, Appl Phys Lett, vol. 104, no. 20, May 2014, doi: 10.1063/1.4879280.##[19] T. Takada, ‘Acoustic and optical methods for measuring electric charge distributions in dielectrics’, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 6, no. 5, pp. 519–547, 1999, doi: 10.1109/CEIDP.1999.804581.##[20] M. Zahn, ‘Transform Relationship between Kerr-effect Optical Phase Shift and Nonuniform Electric Field Distributions’, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 1, no. 2, pp. 235–246, 1994, doi: 10.1109/94.300256.##[21] R. Tobazeon, M. Haidara, and P. Atten, ‘Ion injection and Kerr plots in liquids with blade-plane electrodes’, J Phys D Appl Phys, vol. 17, no. 6, pp. 1293–1301, 1984, doi: 10.1088/0022-3727/17/6/025.##[22] E. Collet, Field Guide to Polarization. Bellingham, WA: SPIE Press, 2005.## ##

Detection of Indoor Building Lighting Fixtures in Point Cloud Data using SDBSCAN 2 2 Building fixtures like lighting are very important to be modelled, especially when a higher level of modelling details is required for planning indoor renovation. LIDAR is often used to capture these details due to its capability to produce dense information. However, this led to the high amount of data that needs to be processed and requires a specific method, especially to detect lighting fixtures. This work proposed a method named Size Density-Based Spatial Clustering of Applications with Noise (SDBSCAN) to detect the lighting fixtures by calculating the size of the clusters and classifying them by extracting the clusters that belong to lighting fixtures. It works based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), where geometrical features like size are incorporated to detect and classify these lighting fixtures. The final results of the detected lighting fixtures to the raw point cloud data are validated by using F1-score and IoU to determine the accuracy of the predicted object classification and the positions of the detected fixtures. The results show that the proposed method has successfully detected the lighting fixtures with scores of over 0.9. It is expected that the developed algorithm can be used to detect and classify fixtures from any 3D point cloud data representing buildings. 260 269 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/242024/12/252024/12/252024/12/262024/12/262024/12/262024/12/262024/12/302024/12/312024/12/312024/12/31 1403/10/11 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/202025/02/222025/02/222025/02/222025/02/222025/02/222025/02/222025/02/222025/03/062025/02/222025/02/20 1403/12/2 Humairah Mansor Humairah Mansor Faculty of Electrical Engineering & Technology and Centre of Excellence for Intelligent Robotics & Autonomous Systems, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Malaysia humairah@unimap.edu.my Shazmin Aniza Abdul Shukor Shazmin Aniza Abdul Shukor Faculty of Electrical Engineering & Technology and Centre of Excellence for Intelligent Robotics & Autonomous Systems, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Malaysia shazmin@unimap.edu.my Razak Wong Chen Keng Razak Wong Chen Keng Geodelta Systems Sdn. Bhd., 22, Jalan SS 20/11, Damansara Utama, Petaling Jaya 47400, Malaysia. Malaysia razwong@geodelta-systems.com Nurul Syahirah Khalid Nurul Syahirah Khalid Faculty of Electrical Engineering & Technology and Centre of Excellence for Intelligent Robotics & Autonomous Systems, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Malaysia syahirahkhalid@unimap.edu.my Clustering Fixtures Heuristic Point Cloud Data Segmentation. [1] Schlosser J., Christopher K. C., and Zsolt K. 2016. “Fusing LIDAR and Images for Pedestrian Detection Using Convolutional Neural Networks.” IEEE Xplore. May 1, 2016. https://doi.org/10.1109/ICRA.2016.7487370.##[2] Wang Y., Anttoni J., Juha H., Jouko L., Harri K., Antero K., Eetu P., and Hannu H. 2016. “Object Classification and Recognition from Mobile Laser Scanning Point Clouds in a Road Environment.” IEEE Transactions on Geoscience and Remote Sensing 54 (2): 1226–39. https://doi.org/10.1109/tgrs.2015.2476502.##[3] A.J., Jdeed. 2019. “Review of Point Clouds Segmentation and Classification Methods for Architectural Objects.”##[4] Fu H., Hao L., Yanqi D., Fu X., and Feixiang C. 2022. “Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN.” Forests 13 (4): 566–66. https://doi.org/10.3390/f13040566.##[5] Huang Z., Yongcai W., Jie W., Peng W., and Xudong C. 2023. “An Object Detection Algorithm Combining Semantic and Geometric Information of the 3D Point Cloud.” Advanced Engineering Informatics 56 (April): 101971– 71. https://doi.org/10.1016/j.aei.2023.101971.##[6] Poux F., Mattes C., and Kobbelt L. . 2020. “Unsupervised Segmentation Of Indoor 3d Point Cloud: Application To Object-Based Classification.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W1-2020 (44): 111–18.##[7] Wijaya C., and Harintaka. 2023. “Analysis and Evaluation of PointNet for Indoor Office Point Cloud Semantic Segmentation.” International Journal on Advanced Science, Engineering and Information Technology/International Journal of Advanced Science, Engineering and Information Technology 13 (6): 2345–53. https://doi.org/10.18517/ijaseit.13.6.18887.##[8] Singh P.S., Iainehborlang M. N., Valarie M., Dibyajyoti C., Victor S., and S. P. Aggarwal. 2023. “Three-Dimensional Point Cloud Segmentation Using a Combination of RANSAC and Clustering Methods.” Current Science 124 (4): 434–41. https://doi.org/10.18520/cs/v124/i4/434-441.##[9] Nguyen A., and Bac L. 2013. “3D Point Cloud Segmentation : A Survey,” no. October 2015. https://doi.org/10.1109/RAM.2013.6758588.##[10] Bool D. L., Mabaquiao L. C., Tupas M. E. , and Fabila J. L. . 2018. “Automated Building Detection Using RANSAC from Classified LiDAr Point Cloud Data.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42 (4/W9): 115–21. https://doi.org/10.5194/isprs-archives-XLII-4-W9-115- 2018.##[11] Isa S. N. M., Shazmin A. S, Rahim N.A , Maarof I , and Yahya Z. R . 2018. “A Review of Data Structure and Filtering in Handling 3D Big Point Cloud Data for Building Preservation.” 2018 IEEE Conference on Systems, Process and Control (ICSPC), no. June 2019: 141–46. https://doi.org/10.1109/SPC.2018.8704136.##[12] Wang R., Jiju P., and Dong C. 2018. “LiDAR Point Clouds to 3D Urban Models : A Review,” no. January. https://doi.org/10.1109/JSTARS.2017.2781132.##[13] Hu, Z., Pei-Long T., Sun-Wei L., and Jian-Ping Z. 2018. “BIM-Based Integrated Delivery Technologies for Intelligent MEP Management in the Operation and Maintenance Phase.” Advances in Engineering Software 115 (January): 1–16. https://doi.org/10.1016/j.advengsoft.2017.08.007.##[14] Adán A., Blanca Q., Samuel A. P., and Frédéric B. 2018. “Scan-to-BIM for ‘Secondary’ Building Components.” Advanced Engineering Informatics 37 (November 2017): 119–38. https://doi.org/10.1016/j.aei.2018.05.001.##[15] Wang B., Chao Y., Han L., Jack C.P. C., and Qian W. 2021. “Fully Automated Generation of Parametric BIM for MEP Scenes Based on Terrestrial Laser Scanning Data.” Automation in Construction 125 (February): 103615. https://doi.org/10.1016/j.autcon.2021.103615.##[16] Ma B., Can Y., Aihua L., Yuxue C., and Lihua C. 2023. “A Faster DBSCAN Algorithm Based on Self- Adaptive Determination of Parameters.” Procedia Computer Science 221 (January): 113–20. https://doi.org/10.1016/j.procs.2023.07.017.##[17] Starczewski A., Piotr G., and Meng J. E. 2020. “A New Method for Automatic Determining of the DBSCAN Parameters.” Journal of Artificial Intelligence and Soft Computing Research 10 (3): 209–21. https://doi.org/10.2478/jaiscr-2020-0014.##[18] Latifi-Pakdehi, Alireza, and Negin D. 2021. “DBHC: A DBSCAN-Based Hierarchical Clustering Algorithm.” Data and Knowledge Engineering 135 (April): 101922. https://doi.org/10.1016/j.datak.2021.101922.##[19] Ahmed K.N., and Abdul R.T. 2016. “An Overview of Various Improvements of DBSCAN Algorithm in Clustering Spatial Databases.” International Journal of Advanced Research in Computer and Communication Engineering 5 (2): 360– 63. https://doi.org/10.17148/IJARCCE.2016.5277.##[20] Chen H., Man L., Wanquan L., Weina W., and Peter X. L. 2022. “An Approach to Boundary Detection for 3D Point Clouds Based on DBSCAN Clustering.” Pattern Recognition 124. https://doi.org/10.1016/j.patcog.2021.108431.##[21] Sokolova M., Nathalie J., and Stan S. 2006. “Beyond Accuracy , F-Score and ROC : A Family of Discriminant Measures for Performance Evaluation Beyond Accuracy , F-Score and ROC : A Family of Discriminant Measures for Performance Evaluation,” no. January. https://doi.org/10.1007/11941439.##[22] Cowton J., Ilias K., and Jaume B. 2019. “Automated Individual Pig Localisation , Tracking And Behaviour Metric Extraction Using Deep Learning.” https://doi.org/10.1109/ACCESS.2019.2933060.##[23] Padilla R., Sergio L. N., and Eduardo A.B.S. 2020. “A Survey on Performance Metrics for Object-Detection Algorithms,” no. July. https://doi.org/10.1109/IWSSIP48289.2020## ##

Prototype Development and Experimental Validation for Wind Energy Harvesting from HVAC System for A Charging Station 2 2 The Heating, Ventilation, and Air Conditioning (HVAC) system is commonly found in buildings such as industrial, commercial, residential, and institutional buildings. This HVAC system generates a significant speed of wind flow from its condenser unit. Surprisingly, this wind energy remains unexploited and thus dissipates into the surroundings. This project aims to leverage this unused wind energy from the condenser unit by developing an energy harvesting prototype that harnesses the HVAC system’s wind for a practical charging station. Specifically, a wind turbine is connected to a three-phase 12 VAC generator motor. This connection would efficiently convert wind energy into electrical power. An energy storage module is also incorporated to ensure uninterrupted functionality for the developed charging station prototype. The energy storage module has a substantial capacity of 25Ah, equivalent to a standard socket outlet. This ensures that the energy storage system can fully charge within three hours if there are no interruptions in the turbine's operation. An experimental validation was conducted by supplying different wind speeds to this project prototype, and it was observed that only when the wind speed is above 10 ms-1 does the energy storage system charge, and sockets provide a consistent output. The final output at the socket provided both 230VAC voltage and a USB charging option, making it versatile for users to charge commonly used electrical appliances such as smartphones and laptops. By repurposing this otherwise wasted wind energy, the developed system prototype contributes to cleaner and more sustainable energy utilization. It also converts unused energy into valuable, cleaner energy. 270 279 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/242024/12/252024/12/252024/12/262024/12/262024/12/262024/12/262024/12/302024/12/312024/12/312024/12/312024/12/31 1403/10/11 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/202025/02/222025/02/222025/02/222025/02/222025/02/222025/02/222025/02/222025/03/062025/02/222025/02/202025/02/20 1403/12/2 Siti Marwangi Mohamad Maharum Siti Marwangi Mohamad Maharum Advanced Telecommunication Technology Research Cluster and Electronics Technology Section, Universiti Kuala Lumpur British Malaysian Institute, 53100 Gombak, Selangor, Malaysia Malaysia sitimarwangi@unikl.edu.my Muhammad Aliff Azim Hamzah Muhammad Aliff Azim Hamzah Electrical Technology Section, Universiti Kuala Lumpur British Malaysian Institute, 53100 Gombak, Selangor, Malaysia Malaysia aliff.hamzah@s.unikl.edu.my Muhammad Ridzwan Ahmad Yusri Muhammad Ridzwan Ahmad Yusri Electrical Technology Section, Universiti Kuala Lumpur British Malaysian Institute, 53100 Gombak, Selangor, Malaysia Malaysia ridzwan.yusri@s.unikl.edu.my Izanoordina Ahmad Izanoordina Ahmad Advanced Telecommunication Technology Research Cluster and Electronics Technology Section, Universiti Kuala Lumpur British Malaysian Institute, 53100 Gombak, Selangor, Malaysia Malaysia izanoordina@unikl.edu.my Charging Booth/Station Power Generation Renewable Energy Sustainable Environment. 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A Review of Analysis of Partial Discharge Measurements using Coupling Capacitor in Rotating Machine 2 2 Partial discharge (PD) is a critical phenomenon in electrical systems, particularly in high-voltage (HV) equipment like transformers, cables, switchgear, and rotating machines. In rotating machines such as generators and motors, PD is a significant concern as it leads to insulation degradation, potentially resulting in catastrophic failure. Effective and reliable diagnostic techniques are essential for detecting and analyzing PD to ensure the operational safety and longevity of such equipment. Various PD detection methods have been developed, including coupling capacitor (CC), high-frequency current transformer (HFCT), and ultra-high frequency (UHF) techniques, each offering unique advantages in assessing the condition of HV electrical systems. Among these, coupling capacitors have gained significant attention due to their ability to improve the accuracy, sensitivity, and efficiency of PD detection in rotating machines. This study focuses on the advancements in coupling capacitor-based techniques and their critical role in enhancing PD diagnostics for monitoring and maintaining high-voltage rotating machinery. 280 290 2024/12/012024/12/052024/12/052024/12/062024/12/062024/12/072024/12/082024/12/152024/12/172024/12/182024/12/192024/12/232024/12/232024/12/232024/12/242024/12/242024/12/252024/12/252024/12/262024/12/262024/12/262024/12/262024/12/302024/12/312024/12/312024/12/312024/12/312024/12/31 1403/10/11 2025/02/182025/02/202025/02/232025/02/232025/02/252025/02/182025/02/202025/02/202025/02/222025/03/012025/02/222025/02/222025/02/202025/02/222025/02/262025/02/202025/02/222025/02/222025/02/222025/02/222025/02/222025/02/222025/02/222025/03/062025/02/222025/02/202025/02/202025/02/22 1403/12/4 Ahmad Syukri Abd Rahman Ahmad Syukri Abd Rahman MHH Condition Monitoring Sdn Bhd, No.8, Tingkat 1, Jalan Sumazau 1J/KU5, Bandar Bukit Raja, 41050 Klang, Selangor, Malaysia. Malaysia ahmadsyukri@studentmail.unimap.edu.my Mohamad Nur Khairul Hafizi Rohani Mohamad Nur Khairul Hafizi Rohani Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia khairulhafizi@unimap.edu.my Nur Dini Athirah Gazata Nur Dini Athirah Gazata Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia diniathirah@studentmail.unimap.edu.my Afifah Shuhada Rosmi Afifah Shuhada Rosmi Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia afifahshuhada@unimap.edu.my Ayob Nazmi Nanyan Ayob Nazmi Nanyan Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia ayobnazmy@unimap.edu.my Aiman Ismail Mohamed Jamil Aiman Ismail Mohamed Jamil Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), 02600 Arau, Perlis, Malaysia. Malaysia aimanismail@studentmail.unimap.edu.my Mohd Helmy Halim Abdul Majid Mohd Helmy Halim Abdul Majid MHH Condition Monitoring Sdn Bhd, No.8, Tingkat 1, Jalan Sumazau 1J/KU5, Bandar Bukit Raja, 41050 Klang, Selangor, Malaysia. Malaysia helmy.uitm@mhhlegacy.com.my Normiza Masturina Samsuddin Normiza Masturina Samsuddin MHH Condition Monitoring Sdn Bhd, No.8, Tingkat 1, Jalan Sumazau 1J/KU5, Bandar Bukit Raja, 41050 Klang, Selangor, Malaysia. Malaysia normiza@mhhlegacy.com.my Partial Discharge Coupling Capacitor (CC) Rotating Machine Ultra-High Frequency (UHF) Phase Resolve Partial Discharge (PRPD). [1] S. Chaudhuri, S. Ghosh, D. Dey, S. Munshi, B. Chatterjee, and S. 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