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A Fast Battery Charger of Solar Vehicle with Maximum Power of Solar Cells Based on Sliding Mode Under 2 2 This paper proposed a control system for the battery charger of a solar vehicle. The battery charger has two parts, boost converter and isolated DC/AC/DC converter. The boost converter is controlled by a proposed control system based on sliding mode. In this controller, the MPPT is implemented by an extreme point of the solar cell P-V curve. Also, the control system of the DC/AC/DC converter is based on sliding mode with consideration of uncertainties of the output filter. A fast charging algorithm based on variable frequencies was carried out by the presented control system and charging of a Lithium-ion battery was done during 20 min from SOC 20% to SOC 80%. The simulation results show control system effectiveness. 1 11 2023/01/09 1401/10/19 2024/03/18 1402/12/28 Abolfazl Masnabadi Abolfazl Masnabadi Arak University of Thechnology Iran abolfazlmasnabadi73@gmail.com Mehdi Asadi Mehdi Asadi Arak University of Technology Iran m.asadi@arakut.ac.ir Solar Vehicle MPPT Isolated DC/AC/DC Converter Sliding Mode Control Uncertainties of the Filter. [1] Tolga Ercan, Nuri C. Onat, Nowreen Keya, Omer Tatari, Naveen Eluru, Murat Kucukvar, Autonomous electric vehicles can reduce carbon emissions and air pollution in cities, Transportation Research Part D: Transport and Environment, Volume 112, 2022, 103472, ISSN 1361-9209.##[2] S. Drid, L. Chrifi-Alaoui, P. Bussy, and M. Ouriagli, "Robust control of the photovoltaic system with improved maximum power point tracking," in 2014 Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER), 2014, pp. 1-7: IEEE.##[3] J. Hahm, J. Baek, H. Kang, H. Lee, and M. Park, "Matlab-based modeling and simulations to study the performance of different MPPT techniques used for photovoltaic systems under partially shaded conditions," International Journal of Photoenergy, vol. 2015, 2015.##[4] D. Verma, S. Nema, A. Shandilya, and S. K. Dash, "Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems," Renewable and Sustainable Energy Reviews, vol. 54, pp. 1018-1034, 2016.##[5] N. Femia, D. Granozio, G. Petrone, G. Spagnuolo, and M. Vitelli, "Predictive & adaptive MPPT perturb and observe method," IEEE Transactions on Aerospace and Electronic Systems, vol. 43, no. 3, pp. 934-950, 2007.##[6] J. Ahmed and Z. Salam, "An improved perturb and observe (P&O) maximum power point tracking (MPPT) algorithm for higher efficiency," Applied Energy, vol. 150, pp. 97-108, 2015.##[7] A. A. Kulaksız and R. Akkaya, "A genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor drive," Solar Energy, vol. 86, no. 9, pp. 2366-2375, 2012.##[8] C. B. Salah and M. Ouali, "Comparison of fuzzy logic and neural network in maximum power point tracker for PV systems," Electric Power Systems Research, vol. 81, no. 1, pp. 43-50, 2011.##[9] N. A. Gounden, S. A. Peter, H. Nallandula, and S. Krithiga, "Fuzzy logic controller with MPPT using line-commutated inverter for three-phase grid-connected photovoltaic systems," Renewable Energy, vol. 34, no. 3, pp. 909-915, 2009.##[10] M. M. Zainuri, M. M. Radzi, A. C. Soh, and N. A. Rahim, "Adaptive P&O-fuzzy control MPPT for PV boost dc-dc converter," in 2012 IEEE International Conference on Power and Energy (PECon), 2012, pp. 524-529: IEEE.##[11] I.-S. Kim, "Robust maximum power point tracker using sliding mode controller for the three-phase grid-connected photovoltaic system," Solar energy, vol. 81, no. 3, pp. 405-414, 2007.##[12] S. Dhar and P. Dash, "A finite time fast terminal sliding mode I–V control of grid-connected PV array," Journal of Control, Automation and Electrical Systems, vol. 26, no. 3, pp. 314-335, 2015.##[13] I.-S. Kim, M.-B. Kim, and M.-J. Youn, "New maximum power point tracker using sliding-mode observer for estimation of solar array current in the grid-connected photovoltaic system," IEEE Transactions on industrial Electronics, vol. 53, no. 4, pp. 1027-1035, 2006.##[14] S. Inamdar, A. Thosar, and S. Mante, "Literature review of 3.3 kW on board charger topologies," in 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), 2019, pp. 276-281: IEEE.##[15] R. C. Cope and Y. Podrazhansky, "The art of battery charging," in Fourteenth Annual Battery Conference on Applications and Advances. Proceedings of the Conference (Cat. No. 99TH8371), 1999, pp. 233-235: IEEE.##[16] T. Instruments, "Simple CC/CV charger using TPS54331," Application Note, SLVA551-December, 2012.##[17] L.-R. Chen, "Design of duty-varied voltage pulse charger for improving Li-ion battery-charging response," IEEE Transactions on Industrial Electronics, vol. 56, no. 2, pp. 480-487, 2008.##[18] L.-R. Chen, S.-L. Wu, D.-T. Shieh, and T.-R. Chen, "Sinusoidal-ripple-current charging strategy and optimal charging frequency study for Li-ion batteries," IEEE Transactions on Industrial Electronics, vol. 60, no. 1, pp. 88-97, 2012.##[19] Pavković, Danijel & Lobrovic, Mihael & Hrgetic, Mario & Komljenović, Ante & Smetko, Viktor. (2014). Battery Current and Voltage Control System Design with Charging Application. 10.1109/CCA.2014.6981481.##[20] H. Vazini, M. Asadi, M. Karimadini, and H. Hajisadeghian, "Sinusoidal charging of Li‐ion battery based on frequency detection algorithm by pole placement control method," IET Power Electronics, vol. 12, no. 3, pp. 421-429, 2019.##[21] J.-G. Chen, Y.-D. Lee, and S.-Y. Park, "Adaptive PI gain control to realize sinusoidal ripple current charging," in 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE-ECCE Asia), 2015, pp. 2582-2589: IEEE.##[22] T. N. Mai, M. Shcherbakov, T. Q. Vinh, N. Shcherbakova, and V. Kamaev, "Hybrid renewable energy systems control based on predictive models and genetic algorithms," Creativity in Intelligent Technologies and Data Science (CIT&DS 2015), Volgograd, Russia, pp. 515-527, 2015.##[23] C.-H. Cheng and J.-X. Ye, "GA-based neural network for energy recovery system of the electric motorcycle," Expert Systems with Applications, vol. 38, no. 4, pp. 3034-3039, 2011.##[24] Y. Chaibi, M. Salhi, and A. El-Jouni, "Sliding mode controllers for standalone PV systems: Modeling and approach of control," International Journal of Photoenergy, vol. 2019, 2019.##[25] A. Masnabadi, M. Asadi, M. Karimadini, and G. Dehnavi, "A Robust Control of a High-Power Isolated Battery Charger with Current Sharing Capability Under Transformer Parameters Uncertainty," Iranian Journal of Science and Technology, Transactions of Electrical Engineering, pp. 1-12, 2021.##[26] M. Gholizadeh and F. R. Salmasi, "Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model," IEEE Transactions on Industrial Electronics, vol. 61, no. 3, pp. 1335-1344, 2013.## ##

Quantitative Analysis of Dual Task Cost Based on Different Cognitive Difficulties 2 2 The impact of cognitive tasks on human movement is of practical significance; we hereby aim to demonstrate that a significant relationship exists between the dual task’s cognitive demand and the disruption caused in hand movement, with the hope to extend this experiment to subjects with disorders (MS, CP, stroke patients) in future studies. By doing so, we hope to be able to develop a metric for evaluating their disease levels using our method and minimize clinical interventions. While previous research has predominantly focused on lower body activities, this study explores the effect of dual tasks on hand movements in healthy individuals. A simulated finger-to-nose test combined with a standard reverse counting task, featuring four difficulty levels, was conducted. Utilizing SVM and decision tree classifiers, we analyzed various features to discern the impact of cognitive tasks on hand movements, including completed cycles and idle time at markers. Our findings reveal that features such as entropy and kurtosis effectively distinguish between task difficulty levels and hand movement disruption. The classifiers achieved accuracies of 70% and 74% for decision tree and SVM, respectively. We hope extending this research to diseased subjects could potentially provide a more accurate assessment of disease severity through the measurement of hand movements during cognitive tasks, offering a non-clinical alternative for disease evaluation. 12 21 2023/01/092023/04/04 1402/1/15 2024/03/182024/03/24 1403/1/5 Abolfazl Karimiyan Abdar Abolfazl Karimiyan Abdar PhD Candidate, Department of Biomedical Engineering and Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Iran akarimiyan@ut.ac.ir Ali Esteki Ali Esteki Professor, Department of Biomedical Engineering and Physics, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Iran aesteki@sbmu.ac.ir Mohsen Sheikh Hassani Mohsen Sheikh Hassani Researcher, Department of Systems and Computer Engineering, Carleton University Canada mohsensheikhhassani@cmail.carleton.ca Hand Movement Analysis Cognitive Task Dual Tasks Finger to Nose Test [1] S. R. Barreca, P. W. Stratford, C. L. Lambert, L. M. Masters, and D. L. Streiner, "Test-retest reliability, validity, and sensitivity of the Chedoke arm and hand activity inventory: a new measure of upper-limb function for survivors of stroke," Archives of physical medicine and rehabilitation, vol. 86, pp. 1616-1622, 2005.##[2] J. C. van den Noort, R. Verhagen, K. J. van Dijk, P. H. Veltink, M. C. Vos, R. M. de Bie, et al., "Quantification of hand motor symptoms in Parkinson’s disease: A proof-of-principle study using inertial and force sensors," Annals of biomedical engineering, vol. 45, pp. 2423-2436, 2017.##[3] B. Post, M. P. Merkus, R. M. de Bie, R. J. de Haan, and J. D. Speelman, "Unified Parkinson's disease rating scale motor examination: are ratings of nurses, residents in neurology, and movement disorders specialists interchangeable?," Movement disorders: official journal of the Movement Disorder Society, vol. 20, pp. 1577-1584, 2005.##[4] M. R. Rodrigues, M. Slimovitch, G. Chilingaryan, and M. F. Levin, "Does the Finger-to-Nose Test measure upper limb coordination in chronic stroke?," Journal of neuroengineering and rehabilitation, vol. 14, p. 6, 2017.##[5] A. Esteki and T. Hodgson, "Quantitative Measurement of Hand’s Action Tremor in Patients with Multiple Sclerosis and the Effects of Thalamic Deep Brain Stimulation," Pajoohandeh Journal, vol. 12, pp. 345-351, 2007.##[6] H. Esmailpour, A. Esteki, and A. Seddighi, "Quantitative assessment of deep brain stimulation on tremor in multiple sclerosis disease," International Clinical Neuroscience Journal, vol. 2, pp. 87-90, 2015.##[7] J. Laczko, R. A. Scheidt, L. S. Simo, and D. Piovesan, "Inter-joint coordination deficits revealed in the decomposition of endpoint jerk during goal-directed arm movement after stroke," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, pp. 798-810, 2017.##[8] R. Osu, K. Ota, T. Fujiwara, Y. Otaka, M. Kawato, and M. Liu, "Quantifying the quality of hand movement in stroke patients through three-dimensional curvature," Journal of neuroengineering and rehabilitation, vol. 8, p. 62, 2011.##[9] B. Hingtgen, J. R. McGuire, M. Wang, and G. F. Harris, "An upper extremity kinematic model for evaluation of hemiparetic stroke," Journal of biomechanics, vol. 39, pp. 681-688, 2006.##[10] S. Alusi, J. Worthington, S. Glickman, L. Findley, and P. Bain, "Evaluation of three different ways of assessing tremor in multiple sclerosis," Journal of Neurology, Neurosurgery & Psychiatry, vol. 68, pp. 756-760, 2000.##[11] N. Notermans, G. Van Dijk, Y. Van der Graaf, J. Van Gijn, and J. Wokke, "Measuring ataxia: quantification based on the standard neurological examination," Journal of Neurology, Neurosurgery & Psychiatry, vol. 57, pp. 22-26, 1994.##[12] S. Alusi, S. Glickman, N. Patel, J. Worthington, and P. Bain, "Target board test for the quantification of ataxia in tremulous patients," Clinical rehabilitation, vol. 17, pp. 140-149, 2003.##[13] R. Krishna, P. N. Pathirana, M. Horne, L. Power, and D. J. Szmulewicz, "Quantitative assessment of cerebellar ataxia, through automated limb functional tests," Journal of neuroengineering and rehabilitation, vol. 16, pp. 1-15, 2019.##[14] K. Du, L. Benavides, E. Isenstein, D. Tadin, and A. Busza, "Reaching Accuracy Assessment in Cerebellar Stroke using Virtual Reality (S20. 010)," ed: AAN Enterprises, 2023.##[15] T. Abe, Y. Yuminaka, M. Sato, and Y. Ikeda, "Evaluation System for Finger-Nose and Finger-Chase Tests on VR Space Using Head-Mounted Display."##[16] H. Negahban, M. R. Hadian, M. Salavati, M. Mazaheri, S. Talebian, A. H. Jafari, et al., "The effects of dual-tasking on postural control in people with unilateral anterior cruciate ligament injury," Gait & posture, vol. 30, pp. 477-481, 2009.##[17] D. S. Speciali, E. M. Oliveira, J. R. Cardoso, J. C. Correa, R. Baker, and P. R. Lucareli, "Gait profile score and movement analysis profile in patients with Parkinson's disease during concurrent cognitive load," Brazilian journal of physical therapy, vol. 18, pp. 315-322, 2014.##[18] T. Asai, S. Misu, T. Doi, M. Yamada, and H. Ando, "Effects of dual-tasking on control of trunk movement during gait: respective effect of manual-and cognitive-task," Gait & posture, vol. 39, pp. 54-59, 2014.##[19] P. Belluck, "Footprints to cognitive decline and Alzheimer’s are seen in gait," New York Times, p. D5, 2012.##[20] N. Polskaia, N. Richer, E. Dionne, and Y. Lajoie, "Continuous cognitive task promotes greater postural stability than an internal or external focus of attention," Gait & posture, vol. 41, pp. 454-458, 2015.##[21] N. Polskaia and Y. Lajoie, "Reducing postural sway by concurrently performing challenging cognitive tasks," Human movement science, vol. 46, pp. 177-183, 2016.##[22] N. Richer, D. Saunders, N. Polskaia, and Y. Lajoie, "The effects of attentional focus and cognitive tasks on postural sway may be the result of automaticity," Gait & Posture, vol. 54, pp. 45-49, 2017.##[23] A. Potvin-Desrochers, N. Richer, and Y. Lajoie, "Cognitive tasks promote automatization of postural control in young and older adults," Gait & posture, vol. 57, pp. 40-45, 2017.##[24] M. C. Dault, A. C. Geurts, T. W. Mulder, and J. Duysens, "Postural control and cognitive task performance in healthy participants while balancing on different support-surface configurations," Gait & posture, vol. 14, pp. 248-255, 2001.##[25] Z. Shiravi, S. T. Moghadam, M. R. Hadian, and G. Olyaei, "Effect of cognitive task on postural control of the patients with chronic ankle instability during single and double leg standing," Journal of bodywork and movement therapies, vol. 21, pp. 58-62, 2017.##[26] G. L. Pellecchia, "Postural sway increases with attentional demands of concurrent cognitive task," Gait & posture, vol. 18, pp. 29-34, 2003.##[27] J. Méjane, J. Faubert, S. Duchêne, and D. R. Labbe, "Evaluating the effect of a perceptual-cognitive task on landing biomechanics of the lower limb," in ISBS-Conference Proceedings Archive, 2015.##[28] S. Tavakoli, S. Forghany, C. Nester, A. Jamali, and K. Bapirzadeh, "The effect of cognitive task on ankle movement variability in athletes with Functional Ankle Instability," in Journal of foot and ankle research, 2014, p. A90.##[29] A. Joshi, S. Kale, S. Chandel, and D. K. Pal, "Likert scale: Explored and explained," British journal of applied science & technology, vol. 7, pp. 396-403, 2015.## ##

Wide-Band Dynamic Load Generator for Emulation of Complex Nonlinear Characteristics of Industrial Loads 2 2 Dynamometers are equipment that has been widely used in the field of electric machines test benches. A dynamometer system has the ability to create intricate and unpredictable behaviours of mechanical loads according to a programmed manner. Extensive research into the characteristics of loads found in industrial settings has shown that non-linear and complex phenomena, including misalignment, mechanical friction, and others, are unavoidable in industrial drive systems. To assess the performance of motor and drive systems in industrial drives when subjected to these non-linear and complex loads, a fast and precise dynamic drive system must track high-frequency torque signals with precision. The suggested dynamometer, serving as an instrumental device, has the ability to emulate a wide torque response across various frequencies during both transient and steady-state conditions for the machine under test. Simulations and experimental results confirm the dynamometer's wide-ranging dynamic response, enabling the emulation of different linear and non-linear loads. 22 31 2023/01/092023/04/042023/05/31 1402/3/10 2024/03/182024/03/242024/02/28 1402/12/9 hossein azizi moghaddam hossein azizi moghaddam Assistant Professor in rotating Electrical Machines Research group, Niroo Research Institute Iran hmoghaddam@nri.ac.ir Arman Farhadi Arman Farhadi Researcher in Niroo Research Institute Iran afarhadi@nri.ac.ir Wide-Band Dynamometer Dynamic Load Emulation Nonlinear Loads Misalignment Crankshaft. [1] Hewson C. R., Sumner M., Asher G. M., and Wheeler P. W., “Dynamic mechanical load emulation test facility to evaluate the performance of AC inverters”. Power Engineering Journal, vol. 14, no. 1, pp. 21-28, Feb. 2000.##[2] Kyslan K. and Durovsky F., “Dynamic Emulation of Mechanical Load – An Approach Based on Industrial Drives Features”. Automatika, vol. 54, no. 3, pp. 356–363, Sept. 2013.##[3] Rodic M., Jezernik K., and Trlep M., “Dynamic emulation of mechanical loads: an advanced approach”. IEE Proceedings - Electric Power Applications, vol. 153, no. 2, pp. 159-166, Sept. 2006.##[4] Arellano-Padilla J., Asher G. M., and Sumner M., “Control of an AC Dynamometer for Dynamic Emulation of Mechanical Loads With Stiff and Flexible Shafts”. IEEE Transactions on Industrial Electronics, vol. 53, no. 4, pp. 1250-1260, June 2006.##[5] Bagh S. K., Samuel P., Sharma R., and Banerjee S., “Emulation of Static and Dynamic Characteristics of a Wind Turbine using Matlab/Simulink,” 2nd Inter. Conf. Power, Control and Embedded Systems, pp. 159–166, Allahabad, India, 2012.##[6] Gan C., Todd R., and Apsley J. M., “Drive System Dynamics Compensator for a Mechanical System Emulator”. IEEE Trans. on Industrial Electronics, vol. 62, no. 1, pp. 70–78, June 2015.##[7] Fajri P., Lee S., Prabhala V. A. K., and Ferdowsi M., “Modeling and Integration of Electric Vehicle Regenerative and Friction Braking for Motor/Dynamometer Test Bench Emulation”. IEEE Transactions on Vehicular Technology, vol. 65, no. 6, pp. 4264-4273, June 2016.##[8] Song-Manguelle J., Ekemb G., Mon-Nzongo D. L., Jin T., and Doumbia M. L., “A Theoretical Analysis of Pulsating Torque Components in AC Machines With Variable Frequency Drives and Dynamic Mechanical Loads”. IEEE Transactions on Industrial Electronics, vol. 65, no. 12, pp. 9311-9324, June 2018.##[9] Akpolat Z. H., Asher G. M., and Clare J. C., “Experimental dynamometer emulation of nonlinear mechanical loads”. IEEE Transactions on Industry Applications, vol. 35, no. 6, pp. 1367-1373, Dec. 1999.##[10] Hakan Akpolat Z., Asher G. M., and Clare J. C., “Dynamic emulation of mechanical loads using a vector-controlled induction motor-generator set” IEEE Transactions on Industrial Electronics, vol. 46, no. 2, pp. 370-379, April 1999.##[11] de Oliveira C. M. R., de Aguiar M. L., de Castro A. G., Guazzelli P. R. U., Pereira W. C. d. A., and Monteiro J. R. B. d. A., “High-Accuracy Dynamic Load Emulation Method for Electrical Drives” IEEE Transactions on Industrial Electronics, vol. 67, no. 9, pp. 7239-7249, Sept. 2020.##[12] S.K. Pillai, A First Course on Electrical Drives, 1nd ed. New Delhi: New Age International, 1990.##[13] AziziMoghaddam H., Farhadi A., and Mohamadian S., “Non-linearity Effects of Industrial Loads on Induction Motor Servo Drive System”. Iranian Journal of Electrical and Electronic Engineering, vol. 18, no. 2, pp. 2046-2046, Nov. 2021.##[14] Bossio J. M., Bossio G. R., and De Angelo C. H., “Angular misalignment in induction motors with flexible coupling,” 35th Annual Conference of IEEE Industrial Electronics, pp. 1033-1038, Porto, Portugal, 2009.##[15] Chen B., Wu Y., and Hsieh F., “Estimation of Engine Rotational Dynamics Using Kalman Filter Based on a Kinematic Model”. IEEE Transactions on Vehicular Technology, vol. 59, no. 8, pp. 3728-3735, Oct. 2010.## ##

Precise Loss Estimation and Comprehensive Thermal Analysis of Axial-Field Flux-Switching PM Machine 2 2 Losses in electric machines produce heat and cause an efficiency drop. As a consequence of heat production, temperature rise will occur which imposes severe problems. Due to the dependence of electrical and mechanical performance on temperature, conducting thermal analysis for a special electric machine that has a compact configuration with poor heat dissipation capability is crucial. This paper aims to carry out the thermal analysis of an axial-field flux-switching permanent magnet (AFFSPM) machine for electric vehicle application. To fulfill this purpose, three-dimensional (3D) finite element analysis is performed to accurately derive electromagnetic losses in active components. Meanwhile, copper losses are calculated by analytic correlation in maximum allowable temperature. To improve thermal performance, cooling blades are inserted on the frame of AFFSPM, and 3D computational fluid dynamics (CFD) is developed to investigate thermal analysis. The effect of different housing materials, the external heat transfer coefficient, and various operating points on the components' temperature has been reported. Finally, 3-D FEA is used to conduct heat flow path and heat generation density. 32 42 2023/01/092023/04/042023/05/312023/06/12 1402/3/22 2024/03/182024/03/242024/02/282024/02/16 1402/11/27 Ali Zarghani Ali Zarghani Shahid Beheshti University Iran alizarghani.97@gmail.com Pedram Dehgoshaei Pedram Dehgoshaei Shahid Beheshti University Iran pedram.dehgosha@gmail.com Hossein Torkaman Hossein Torkaman Shahid Beheshti University Iran h_torkaman@sbu.ac.ir Aghil Ghaheri Aghil Ghaheri Shahid Beheshti University Iran A_ghaheri@sbu.ac.ir Flux Switching Machine Cooling System Computational Fluid Dynamics Finite Element Analysis Thermal Analysis. [1] F. Farrokh, A. Vahedi, H. Torkaman, and M. Banejad, "Design and comparison of dual‐stator axial‐field flux‐switching permanent magnet motors for electric vehicle application," IET Electrical Systems in Transportation, vol. 13, no. 2, p. e12074, 2023.##[2] J. Faiz and M. Maktobian, "Performance Enhancement of Flux Switching Motor for Electric Vehicle Applications: An Overview," IET Electrical Systems in Transportation, vol. 2024, p. 9071667, 2024.##[3] A. Darjazini, A. Vahedi, S. Gharehseyed, and A. Nobahari, "A modified approach for efficient cogging torque suppression in a flux switching permanent magnet generator used in micro‐scale wind turbines," IET Electric Power Applications, 2023.##[4] W. Yu, W. Hua, and Z. Zhang, "Cooling Analysis of High-Speed Stator-Permanent Magnet Flux-Switching Machines for Fuel-Cell Electric Vehicle Compressor," IEEE Transactions on Vehicular Technology, vol. 71, no. 1, pp. 210-219, 2021.##[5] A. Ghaheri, E. Afjei, and H. Torkaman, "Design optimization of a novel linear transverse flux switching permanent magnet generator for direct drive wave energy conversion," Renewable Energy, vol. 198, pp. 851-860, 2022.##[6] E. F. Farahani, N. J. Baker, and F. Mahmouditabar, "An Innovative H-Type Flux Switching Permanent Magnet Linear Generator for Thrust Force Enhancement," Energies, vol. 16, no. 16, p. 5976, 2023.##[7] L. Shao, R. Navaratne, M. Popescu, and G. Liu, "Design and Construction of Axial-Flux Permanent Magnet Motors for Electric Propulsion Applications—A Review," IEEE Access, vol. 9, pp. 158998-159017, 2021.##[8] A. Ghaheri, E. Afjei, and H. Torkaman, "A novel axial air-gap transverse flux switching PM generator: Design, simulation and prototyping," IET Electric Power Applications, vol. 17, no. 4, pp. 452-463, 2023.##[9] A. Ghaheri, A. Mohammadi Ajamloo, H. Torkaman, and E. Afjei, "Design, modelling and optimisation of a slot‐less axial flux permanent magnet generator for direct‐drive wind turbine application," IET Electric Power Applications, vol. 14, no. 8, pp. 1327-1338, 2020.##[10] H. Torkaman, A. Ghaheri, and A. Keyhani, "Design of rotor excited axial flux-switching permanent magnet machine," IEEE Trans. Energy Convers., vol. 33, no. 3, pp. 1175-1183, 2018.##[11] H. Torkaman, A. Ghaheri, and A. Keyhani, "Axial flux switched reluctance machines: a comprehensive review of design and topologies," IET Electric Power Applications, vol. 13, no. 3, pp. 310-321, 2019.##[12] M. Farahzadi, K. Abbaszadeh, and S. Mirnikjoo, "Electromagnetic-Thermal Analysis of a Hybrid-Excited Flux Switching Permanent Magnet Generator for Wind Turbine Application," IEEE Transactions on Energy Conversion, pp. 1-12, 2023.##[13] F. Mahmouditabar, A. Vahedi, and N. Takorabet, "Design and analysis of interior permanent magnet motor for electric vehicle application considering irreversible demagnetization," IEEE Transactions on Industry Applications, vol. 58, no. 1, pp. 284-293, 2021.##[14] F. Mahmouditabar, A. Vahedi, and F. Marignetti, "The Demagnetization Phenomenon in PM Machines: Principles, Modeling, and Design Considerations," IEEE Access, vol. 11, pp. 47750-47773, 2023.##[15] G. Davarpanah, J. Faiz, H. Shirzad, and M. Lotfizadeh, "A Modular Hybrid Excited Switched Reluctance Motor with Two Groups of Permanent Magnets to Enhance the Performance of the Motor," IEEE Transactions on Energy Conversion, pp. 1-12, 2024.##[16] A. Zarghani, A. Ghaheri, M. Abolghasemi, E. Afjei, and H. Torkaman, "Thermal Modeling and Analysis of Transverse-Radial Flux Magnetic Gear," in 2023 3rd International Conference on Electrical Machines and Drives (ICEMD), 20-21 Dec. 2023 2023, pp. 1-7, doi: 10.1109/ICEMD60816.2023.10429212. ##[17] D. A. Howey, P. R. Childs, and A. S. Holmes, "Air-gap convection in rotating electrical machines," IEEE Transactions on Industrial Electronics, vol. 59, no. 3, pp. 1367-1375, 2010.##[18] A. Zarghani, H. Torkaman, N. Arbab, and M. S. Toulabi, "Lumped parameter thermal network for thermal analysis of a rotor-excited axial flux switching machine with electromagnetic-thermal design," Measurement, vol. 193, p. 110971, 2022.##[19] X. Cai, M. Cheng, S. Zhu, and J. Zhang, "Thermal modeling of flux-switching permanent-magnet machines considering anisotropic conductivity and thermal contact resistance," IEEE Transactions on Industrial Electronics, vol. 63, no. 6, pp. 3355-3365, 2016.##[20] A. Zarghani, M. Farahzadi, A. Ghaheri, K. Abbaszadeh, H. Torkaman, and E. Afjei, "2D Lumped Parameter Model for Temperature Prediction in a Radial Flux Switching Generator with Two Permanent Magnet Types," in 2023 3rd International Conference on Electrical Machines and Drives (ICEMD), 20-21 Dec. 2023 2023, pp. 1-6, doi: 10.1109/ICEMD60816.2023.10429503. ##[21] A. Zarghani, S. M. Saghin, A. Ghaheri, E. Afjei, and H. Torkaman, "Magneto-Thermal Analysis of a Novel Excited Outer Rotor Flux-Switching PM Machine," in 14th Power Electronics & Drives: Systems and Technologies Conference (PEDSTC), Babol, Iran, 31 Jan.-2 Feb. 2023 (2023), pp. 1-6. ##[22] G. Zhang, W. Hua, M. Cheng, B. Zhang, and X. Guo, "Coupled magnetic-thermal fields analysis of water cooling flux-switching permanent magnet motors by an axially segmented model," IEEE Transactions on Magnetics, vol. 53, no. 6, pp. 1-4, 2017.##[23] P. Dehgosha, A. Zarghani, H. Torkaman, and A. Ghaheri, "Three-Dimensional Thermal Analysis of a Rotor-Excited Axial Flux Switching Permanent Magnet Machine by Computational Fluid Dynamics Method," in 2023 3rd International Conference on Electrical Machines and Drives (ICEMD), 20-21 Dec. 2023 2023, pp. 1-6, doi: 10.1109/ICEMD60816.2023.10429151. ##[24] Y. C. Chong, E. J. E. Subiabre, M. A. Mueller, J. Chick, D. A. Staton, and A. S. McDonald, "The ventilation effect on stator convective heat transfer of an axial-flux permanent-magnet machine," IEEE Transactions on Industrial Electronics, vol. 61, no. 8, pp. 4392-4403, 2013.##[25] D. A. Howey, A. S. Holmes, and K. R. Pullen, "Measurement and CFD prediction of heat transfer in air-cooled disc-type electrical machines," IEEE Transactions on Industry Applications, vol. 47, no. 4, pp. 1716-1723, 2011.##[26] L. Veg and J. 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A Dependable Protection Scheme for Electrical Vehicle Integrated Microgrid Considering Stressed Fault Scenarios and Dissimilar Fault Inceptions 2 2 In recent years, due to the widespread applications of DC power-based appliances, the researchers attention to the adoption of DC microgrids are continuously increasing. Nevertheless, protection of the DC microgrid is still a major challenge due to a number of protection issues, such as pole-to-ground and pole-to-pole faults, absence of a zero crossing signal, magnitude of the fault current during grid-connected and islanded mode, bidirectional behaviour of converters, and failure of the converters due to enormous electrical stress in the converter switches which are integrated in the microgrid.  Failure of the converter switches can interrupt the charging of the electrical vehicles in the charging stations which can affect transportation facilities. In addition to the above mentioned issues protection of the DC microgrid is more challenging when fault parameters are varying due to dissimilar grounding conditions and varying operational dynamics of the renewable sources of energy. Motivated by the above challenges a support vector machine and ensemble of k-nearest neighbor based protection scheme has been proposed in this paper to accurately detect and classify faults under both of the modes of operation. Results in the section 5 indicate that performance of the protection scheme is greater as compared to other algorithms. 43 55 2023/01/092023/04/042023/05/312023/06/122023/07/02 1402/4/11 2024/03/182024/03/242024/02/282024/02/162024/03/07 1402/12/17 Shankarshan Prasad Tiwari Shankarshan Prasad Tiwari National Institute of Technology Raipur, Chhattisgarh, India India shankarshan.tiwari@siem.org.in DC Microgrid Fault Detection Support Vector Machine Ensemble of k-Nearest Neighbor Grid Connected and Islanded Mode [1] M. Uddin, H. Mo, D. Dong, S. Elsawah, J. Zhu, and J.M. Guerrero, "Microgrids: A review, outstanding issues and future trends," Energy Strategy Reviews, Vol. 49: pp.101127, 2023##[2] G.P. Santos, A. Tsutsumi, and J.C.M. Vieira, "Enhanced voltage relay for AC microgrid protection," Electric Power Systems Research, Vol. 220, pp.109310, 2023##[3] S.P. Tiwari, "Fault detection in ring based smart LVDC microgrid using ensemble of decision tree," Iranian Journal of Electrical and Electronic Engineering Vol. 18, No. 4, pp.2600-2600, 2022##[4] N. Poursafar, S. Taghizadeh, M.J. Hossain, and F. Blaabjerg "An enhanced control strategy for an ultra-fast EV charging station in a DC microgrid," International Journal of Electrical Power & Energy Systems, Vol. 146, pp.108727, 2023##[5] N. Bayati, H.R. Baghaee, A. Hajizadeh, M. Soltani, and Z. Lin, “Mathematical morphology-based local fault detection in DC Microgrid clusters,” Electric Power System Research, Vol.192, pp.106981, 2021##[6] Z. Zhang, C. Qing, X. Ranran, and S. Kongming, “The fault analysis of PV cable fault in DC microgrids,” IEEE Trans. on Energy Conversion, Vol. 34, No.1, pp.486-496.2018.##[7] J.C. Ciezki, and R.W. Ashton,“Selection and stability issues associated with a navy shipboard DC zonal electric distribution system,” IEEE Transactions on Power Delivery, Vol. 15, No.2, pp.665-669, 2000.##[8] OVG Swathika, and S. Hemamalini, “Prims-Aided Dijkstra algorithm for adaptive protection in microgrids,” IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol.4, No.4, pp.1279-1286, 2016##[9] S. Sharma, and M. Tripathy, "Differential reactor voltage based fault detection and classification for smart DC microgrid," IEEE Transactions on Industrial Informatics, Vol. 19, No. 12, pp. 11730-11741, 2023##[10] P. Chauhan, C.P. Gupta, and M. Tripathy. "High speed fault detection and localization scheme for low voltage DC microgrid," International Journal of Electrical Power & Energy Systems, Vol. 146, pp.108712, 2023##[11] S. Sarangi, C. Biswal, B. K. Sahu, I.S. Samanta, and P. K. Rout, "Fault detection technique using time-varying filter-EMD and differential-CUSUM for LVDC microgrid system," Electric Power Systems Research, Vol. 219, pp.109254, 2023##[12] Z. Zhou, J. Jiang, S. Ye, D. Yang and J. Jiang,"Novel bidirectional O-Z-source circuit breaker for DC microgrid protection," IEEE Transactions on Power Electronics, Vol. 36, no. 2, pp. 1602-1613, 2021##[13] A. Shabani and K. Mazlumi,"Evaluation of a communication-assisted overcurrent protection scheme for photovoltaic-based DC microgrid," IEEE Transactions on Smart Grid, Vol. 11, No. 1, pp. 429-439, 2020 ##[14] M. Shamsoddini, B. Vahidi, R. Razani, and Y. A.R.I. Mohamed, "A novel protection scheme for low voltage DC microgrid using inductance estimation," International Journal of Electrical Power & Energy Systems, Vol.120, pp.105992, 2020##[15] P. Chauhan, C.P. Gupta, and M. Tripathy, "A novel adaptive protection technique based on rate-of-rise of fault current in DC microgrid," Electric Power Systems Research, Vol. 207, pp. 107832, 2022##[16] M.R.K. Rachi, M.A. Khan and I. Husain, "Local measurement-based protection coordination system for a standalone DC microgrid." IEEE Transactions on Industry Applications, Vol. 57, No. 5, pp. 5332-5344, 2021##[17] M. Salehi, S. A. Taher, I. Sadeghkhani and M. Shahidehpour, "A poverty severity index-based protection strategy for ring-bus low-voltage DC microgrids,” IEEE Transactions on Smart Grid, Vol. 10, No. 6, pp. 6860-6869, Nov. 2019,##[18] S. A. Wakode, M. S. Ballal, A. A. Sheikh and R. R. Deshmukh, "Oscillation frequency component-based protection scheme for DC microgrid,” IEEE Transactions on Industry Applications, Vol. 57, No. 6, pp. 5747-5757, 2021 ##[19] M. Shamsoddini, B. Vahidi, R. Razani, and H. Nafisi, "Extending protection selectivity in low voltage DC microgrids using compensation gain and artificial line inductance," Electric Power Systems Research, Vol.188, pp.106530, 2020##[20] N. Bayati, H.R. Baghaee, M. Savaghebi, A. Hajizadeh, M. Soltani, and Z. Lin. "EMD/HT‐based local fault detection in DC microgrid clusters." IET Smart Grid, Vol.5, No. 3, pp.177-188, 2022.##[21] Wang, Xiaodong, Ruojin Wang, Yingming Liu, and Xing Gao, "Impedance ground faults detection and classification method for DC microgrid," Journal of Electrical Engineering & Technology, pp.1-13, 2023##[22] V. F. Couto and M. Moreto, "High impedance fault detection on microgrids considering the impact of VSC based generation," IEEE Access, Vol. 11, pp. 89550-89560, 2023##[23] W. Zhang, H. Zhang and N. Zhi, "A novel protection strategy for DC microgrid considering communication failure," Energy Reports, Vol.9, pp.2035-2044, 2023##[24] X. Xu, J. Ye, Y. Wang, X. Xu, Z. Lai and X. Wei, "Design of a reliable bidirectional solid-state circuit breaker for DC microgrids,” IEEE Transactions on Power Electronics, vol. 37, no. 6, pp. 7200-7208, June 2022##[25] J.M. Johnson, and Anamika Yadav, "Complete protection scheme for fault detection, classification and location estimation in HVDC transmission lines using support vector machines." IET Science, Measurement & Technology, Vol.11, No. 3, 2017, pp.279-287.##[26] J Wang, D Gao, S Zhu, S Wang, H Liu. "Fault diagnosis method of photovoltaic array based on support vector machine." Energy sources, part a: recovery, utilization, and environmental effects, Vol.45, No. 2, pp.5380-5395.##[27] S.P. Tiwari, “An efficient protection scheme for wind integrated microgrid considering dissimilar AC Faults and varying fault resistance,” Iranian Journal of Electrical & Electronic Engineering, Vol.19, No.3. pp. 152-164, 2023## ##

Spectrum Sensing Optimization Using De-noising and Energy Detection 2 2 Cognitive radio (CR) is an effective technique for dealing with scarcity in spectrum resources and enhancing overall spectrum utilization. CR attempts to enhance spectrum sensing by detecting the primary user (PU) and allowing the secondary user (SU) to utilize the spectrum holes. The rapid growth of CR technology increases the required standards for Spectrum Sensing (SS) performance, especially in regions with low Signal-to-Noise Ratios (SNRs). In Cognitive Radio Networks (CRN), SS is an essential process for detecting the available spectrum. SS is divided into sensing time and transmission time; the more the sensing time, the higher the detection probability) and the lower the probability of a false alarm). So, this paper proposes a novel two-stage SS optimization model for CR systems. The proposed model consists of two techniques: Interval Dependent De-noising (IDD) and Energy Detection (ED), which achieve optimum sensing time, maximum throughput, lower and higher. The Simulation results demonstrated that the proposed model decreases the, achieves a higher especially at low SNRs ranging, and obtains the optimum sensing time, achieving maximum throughput at different numbers of sensing samples (N) and different SNRs from -10 to -20 dB in the case of N = 1000 to 10000 samples. The proposed model achieves a throughput of 5.418 and 1.98 Bits/Sec/HZ at an optimum sensing time of 0.5ms and 1.5ms respectively, when N increases from 10000 to 100000 samples. The proposed model yields an achievable throughput of 5.37 and 4.58 Bits/Sec/HZ at an optimum sensing time of 1.66ms and 13ms respectively. So, it enhances the SS process than previous related techniques. 56 71 2023/01/092023/04/042023/05/312023/06/122023/07/022023/08/15 1402/5/24 2024/03/182024/03/242024/02/282024/02/162024/03/072024/02/28 1402/12/9 mohamed khalaf mohamed khalaf Mohamed Khalaf , Department of Electronics and Communications Engineering, Modern Academy for Engineering and Technology, Maadi, Egypt, mohamedkhalafabdelbadee@gmail.com Egypt mohamedkhalafabdelbadee@gmail.com Ahmed Fawzi Ahmed Fawzi Ahmed Fawzi , Department of Electronics and Communications Engineering, Modern Academy for Engineering and Technology, Maadi, Egypt, Ahmed.fawzy@eng.modern-academy.edu.eg Egypt Ahmed.fawzy@eng.modern-academy.edu.eg Ahmed yahya Ahmed yahya Ahmed Yahya, Electrical Engineering Department, Faculty of Engineering (Cairo), Al-Azhar University, Cairo, Egypt dr.ahmedyahya@azhar.edu.eg Egypt dr.ahmedyahya@azhar.edu.eg CR IDD Optimization Optimum Sensing Time Maximum Throughput [1] Kaabouch, Naima, ed. Handbook of research on software–defined and cognitive radio technologies for dynamic spectrum management. IGI global, 2014.‌##[2] I. Develi, “Spectrum sensing in cognitive radio networks: threshold optimization and analysis,” EURASIP Journal on Wireless Communications and Networking, vol. 255, 2020.##[3] Salahdine, Fatima, and Hassan El Ghazi, “A real time spectrum scanning technique based on compressive sensing for cognitive radio networks,”2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). IEEE, 2017.‌##[4] Yucek, Tevfik, and Huseyin Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE communications surveys & tutorials, 11.1, pp. 116–130, 2009.##[5] L. Lu, X. Zhou, U. Onunkwo, and G. Li, “Ten years of research in spectrum sensing and sharing in cognitive radio,” EURASIP J. Wireless Communication. Netw., vol. 2012, no. 1, p. 28, 2012.##[6] Reyes, Hector, et al, “A spectrum sensing technique based on autocorrelation and Euclidean distance and its comparison with energy detection for cognitive radio networks,”Computers & Electrical Engineering, 52, pp. 319–327, 2016.‌##[7] Lu, Xiao, et al, “Dynamic spectrum access in cognitive radio networks with RF energy harvesting,” IEEE Wireless Communications, 21.3, pp. 102–110, 2014.##[8] Budaraju, Sriramachandra Murthy, and Marcharla Anjaneyulu Bhagyaveni, “A novel energy detection scheme based on channel state estimation for cooperative spectrum sensing,” Computers & Electrical Engineering, 57, pp. 176–185, 2017.##[9] Armi, Nasrullah, Mohd Zuki Yusoff, and Naufal M. Saad, “Cooperative spectrum sensing in decentralized cognitive radio system,” Eurocon 2013, IEEE, pp.113–118, 2013.## [10] L.–L. ZHANG, J. G. HUANG and C. K. TANG “Novel Energy Detection Scheme in Cognitive Radio,” IEEE conference on Signal Processing, Communications and Computing (ICSPCC), pp.1–4, 2011.##[11] Ogbodo, Emmanuel U., David Dorrell, and Adnan M. Abu–Mahfouz, “Cognitive radio based sensor network in smart grid: architectures, applications and communication technologies,” IEEE Access, 5, pp. 19084–19098, 2017.‌##[12] Bagwari, Ashish, and G. Singh Tomar, “Improved spectrum sensing technique using multiple energy detectors for cognitive radio networks,” International Journal of Computer Applications, 62.4, pp. 11–21, 2013.##[13] Gorcin, Ali, et al, “An adaptive threshold method for spectrum sensing in multi–channel cognitive radio networks,”2010 17th International Conference on Telecommunications, IEEE, pp. 425–429, 2010.##[14] Tandra, Rahul, and Anant Sahai, “SNR walls for signal detection,” IEEE Journal of selected topics in Signal Processing, 2.1, pp. 4–16, 2008.##[15] Hamid, Mohamed, Niclas Björsell, and Slimane Ben Slimane, “Energy and eigenvalue based combined fully blind self–adapted spectrum sensing algorithm,” IEEE Transactions on Vehicular Technology, 65.2, pp. 630–642, 2016.##[16] A. Fawzi, W. El–Shafai, M. Abd–Elnaby, A. Zekry, and F. E. Abd El–Samie, “Adaptive two–stage spectrum sensing model using energy detection and wavelet denoising for cognitive radio systems,” International Journal of Communication Systems, 33.16, e4400, 2020.##[17] Bagwari, Ashish, et al, “A robust detector using snr with adaptive threshold scheme in cognitive radio networks,” International Journal of Signal Processing, Image Processing and Pattern Recognition, 9.5, pp. 173–186, 2016.##[18] Rabie Mohamed, Alaa, Ahmad A. Aziz El–Banna, and Hala A. Mansour, “Multi–path hybrid spectrum sensing in cognitive radio,” Arabian Journal for Science and Engineering, pp. 1–8, 2021##[19] Kanti, Jyotshana, and Geetam Singh Tomar, “of sensing failure problem: an improved two–stage detector,” The Computer Journal, 61.6, pp. 847–855, 2018.##[20] Wasonga, Fidel, Thomas O. Olwal, and Adnan Abu–Mahfouz, “Improved two–stage spectrum sensing for cognitive radio networks,”Journal of Advanced Computational Intelligence and Intelligent Informatics, 23.6, pp. 1052–1062, 2019.##[21] Mashta, Faten, Wissam Altabban, and Mohieddin Wainakh, “Two–Stage Spectrum Sensing for Cognitive Radio Using Eigenvalues Detection,” International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 12.4, pp. 18–36, 2020.‌##[22] Usman, Mustefa Badri, Ram Sewak Singh, and S. Rajkumar, “Stage Spectrum Sensing Technique for Cognitive Radio Network Using Energy and Entropy Detection,” Wireless Power Transfer, 2022.##[23] Ali, Alaa, Ahmad A. Aziz El–Banna, and Hala A. Mansour, “Reciprocal Two Stages Spectrum Sensor to overcome the Noise Uncertainty,” The International Journal for Engineering and Modern Science, 1.1, pp. 1–10, 2022.##[24] Anaand, Prem Prakash, and Chhagan Charan, “Two stage spectrum sensing for cognitive radio networks using ED and AIC under noise uncertainty,” international conference on recent trends in information technology (ICRTIT). IEEE, pp. 1-6, 2016.‌##[25] Aparna Singh Kushwah, Rohit Parashar, “Performance Analysis of Two-Stage Spectrum Sensing for Cognitive Radio Networks,” International Journal of Electronics & Communication Technology,Vol. 7, Issue 3, July – Sept. 2016.##[26] van Bloem, Jan–Willem, Roel Schiphorst, and Cornelis H. Slump, “Removing non–stationary noise in spectrum sensing using matrix factorization,” EURASIP journal on advances in signal processing, 2013.1, pp. 1–19, 2013.##[27] Martinek, Radek, and Jan Zidek, “The real implementation of NLMS channel equalizer into the system of software defined radio,” Advances in Electrical and Electronic Engineering, 10.5, pp. 330–336, 2012.##[28] Das, Aritra, et al, “An improved energy detector for spectrum sensing in cognitive radio system with adaptive noise cancellation and adaptive threshold,” Computational Advancement in Communication Circuits and Systems: Proceedings of ICCACCS 2014. Springer India, pp.113–119, 2015##[29] Sonnenschein, Alexander, and Philip M. Fishman, “detection of spread–spectrum signals in noise of uncertain power,” IEEE Transactions on Aerospace and Electronic Systems, 28.3, pp. 654–660, 1992.##[30] Quadri, Adnan, M. 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IGI global, 2014.‌##[34] Mallat, “A Wavelet Tour of Signal Processing,” Academic Press, San Diego, USA, 1998.##[35] Rioul, Olivier, and Martin Vetterli, “Wavelets and signal processing,” IEEE signal processing magazine, 8.4, pp. 14–38, 1991.##[36] Kumar, Abhishek, Seemanti Saha, and Rajarshi Bhattacharya, “Wavelet transform based novel edge detection algorithms for wideband spectrum sensing in CRNs,” AEU–International Journal of Electronics and Communications, 84, pp. 100–110, 2018.##[37] Teolis, Anthony, and John J. Benedetto, “Computational signal processing with wavelets,”Vol. 182. Boston, MA, USA: Birkhäuser, 1998.‌##[38] Mittermayr, C. R., et al, “Wavelet denoising of Gaussian peaks: a comparative study,” Chemometrics and Intelligent Laboratory Systems, 34.2, pp. 187–202, 1996##[39] Donoho, David L, “De–noising by soft–thresholding,” IEEE transactions on information theory, 41.3, pp. 613–627, 1995.##[40] Padmavathi, G., and S. 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Frequency Grounding System Effect on the EGLAs Placement in a 400 kV Transmission Line 2 2 In this paper, the performance of the EGLA (Externally Gaped Line Arresters) and its impact on the back flashover rate of a 400 kV transmission line have been investigated. The frequency behavior of the grounding system and soil resistivity has been modeled. To analyze the EGLA performance in relation to the grounding system's frequency behavior, a rod-shaped grounding system model has been implemented. By placing the EGLA at different phases of the transmission line, the best scenario has been identified to minimize back-flashover occurrences. Furthermore, the performance of the frequency grounding system to that of the nonlinear grounding system has been compared. The results clearly indicate that using a nonlinear grounding system leads to higher back flashover rates compared to the frequency grounding system. Additionally, the EGLA absorbs less energy when connected to a nonlinear resistor compared to the frequency grounding system. It can be concluded that modeling the grounding system's frequency behavior using the frequency grounding model provides more accurate results, especially in investigations related to power grid insulation coordination. 72 82 2023/01/092023/04/042023/05/312023/06/122023/07/022023/08/152023/09/08 1402/6/17 2024/03/182024/03/242024/02/282024/02/162024/03/072024/02/282024/02/18 1402/11/29 Masume Khodsuz Masume Khodsuz Faculty of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran Iran m.khodsouz@mazust.ac.ir Externally Gapped Line Arrester Frequency Grounding System Lightning Performance Transmission Line. [1] Y. Zhou, Y. Xie, D. Zhang, N. Dong, Y. Chen, and Y. Jing, “Response of 10-kV metal-oxide surge arresters excited by nanosecond-level transient electromagnetic disturbances,” IEEE Transactions on Electromagnetic Compatibility, Vol. 63, No. 2, pp. 614–621, 2020.##[2] M. Khodsuz, “Externally gapped line arrester performance in high voltage transmission line using frequency grounding system: Absorbed energy and expected life assessment,” IET Science, Measurement and Technology, Vol. 16, No. 7, pp. 426–440, 2022.##[3] M. Khodsuz, “Lightning Evaluation of Overhead Transmission Line Protected by EGLA,” Iran. J. Electr. Electron. Eng., Vol. 18, No. 2, pp. 2370–2370, 2022.##[4] K. E. Merrill and G. T. Heydt, “The calculation of energy dissipation in metal oxide varistors for power distribution applications,” IEEE Transactions on Power Systems, Vol. 34, No. 5, pp. 3967–3969, 2019##[5] T. H. Pham, S. A. Boggs, H. Suzuki, and T. Imai, “Effect of externally gapped line arrester placement on insulation coordination of a twin-circuit 220 kV line,” IEEE Transaction on. Power Delivery, Vol. 27, No. 4, pp. 1991–1997, 2012, ##[6] S. Visacro, F. H. Silveira, and A. De Conti, “The use of underbuilt wires to improve the lightning performance of transmission lines,” IEEE Transaction on. Power Delivery, Vol. 27, No. 1, pp. 205–213, 2011##[7] J. A. Martinez and F. Castro-Aranda, “Lightning performance analysis of overhead transmission lines using the EMTP,” IEEE Transaction on. Power Delivery, Vol. 20, No. 3, pp. 2200–2210, 2005##[8] M. E. Ahmadi, M. Niasati, and M. R. Barzegar‐Bafrooei, “Enhancing the lightning performance of overhead transmission lines with optimal EGLA and downstream shield wire placement in mountainous areas: a complete study,” IET Science, Measurement and Technology, Vol. 14, No. 5, pp. 564–575, Jul. 2020##[9] M. Khodsuz, “Externally gapped line arresters placement effect on transmission line lightning performance including the frequency response of the grounding system,” IET Generation, Transmission and Distribution., Vol. 17, No. 22, pp. 4975–4990, Nov. 2023##[10] F. Giraudet, “Various benefts for line surge arrester application and advantages of externally gapped line arresters,” in 2019 International Conference on High Voltage Engineering and Technology (ICHVET), IEEE, , pp. 1–6, 2019##[11] A. H. A. Bakar, D. N. A. Talib, H. Mokhlis, and H. A. Illias, “Lightning back flashover double circuit tripping pattern of 132 kV lines in Malaysia,” International Journal of Electrical Power & Energy Systems, Vol. 45, No. 1, pp. 235–241, 2013##[12] D. Stanchev, “Model study of lightning overvoltages in substation due to back flashover of overhead transmission line 220 kV,” in 2018 10th Electrical Engineering Faculty Conference (BulEF), IEEE, 2018##[13] D. Wang, B. He, W. Zhong, B. Lin, D. Wang, and T. 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Savić, “Some aspects of overhead transmission lines lightning performance estimation in engineering practice: LIGHTNING TRANSIENTS MODELING,” International Transactions on Electrical Energy Systems, Vol. 26, No. 1, pp. 79–93, Jan. 2016##[18] R. O. Caldwell and M. Darveniza, “Experimental and analytical studies of the effect of non-standard waveshapes on the impulse strength of external insulation,” IEEE Transactions on Power Apparatus and Systems, Vol. 92, No. 4, pp. 1420–1428, 1973##[19] J. A. Martinez and D. W. Durbak, “Parameter determination for modeling systems transients-Part V: Surge arresters,” IEEE Transaction on Power Delivery., Vol. 20, No. 3, pp. 2073–2078, 2005##[20] S. Visacro, R. Alipio, M. H. M. Vale, and C. Pereira, “The response of grounding electrodes to lightning currents: The effect of frequency-dependent soil resistivity and permittivity,” IEEE Transactions on Electromagnetic Compatibility, Vol. 53, No. 2, pp. 401–406, 2011.##[21] K. Sheshyekani, M. Akbari, B. 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SUBCOMMITTEE, “MODELING OF METAL OXIDE SURGE ARRESTERS IEEE WORKING GROUP 3.4. 11 APPLICATION OF SURGE PROTECTIVE DEVICES SUBCOMMITTEE SURGE PROTECTIVE DEVICES COMMITTEE”.##[27] J. Woodworth, “Externally gapped line arresters a critical design review,” in 2014 IEEE PES T&D Conference and Exposition, IEEE, pp. 1–5, 2014##[28] E. Partal, M. S. Sert, M. Ger, and F. Giraudet, “Effective lightning mitigation method on unshielded distribution line by using high charge ratings externally gapped line arresters (EGLA),” in 27th International Conference on Electricity Distribution (CIRED 2023), Rome, Italy: Institution of Engineering and Technology, pp. 1065–1069, 2023##[29] T. Tsuboi, J. Takami, S. Okabe, K. Inami, and K. Aono, “Aging effect on insulation reliability evaluation with Weibull distribution for oil-immersed transformers,” IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 17, No. 6, pp. 1869–1876, 2010## ##

Proposing Very Low Power Three-Valued Flip-Flops by Using CNTFET Transistors 2 2 The scaling limitations of Complementary Metal-Oxide-Semiconductor (CMOS) transistors to achieve better performance have led to the attention of other structures to improve circuit performance. One of these structures is multi-valued circuits. In this paper, we will first study Carbon Nanotube Transistors (CNT). CNT transistors offer a viable means to implement multi-valued logic due to their variable and controllable threshold voltage. Subsequently, we delve into the realm of three-valued flip-flop circuits, which find extensive utility in digital electronics. Leveraging the insights gained from our analysis, we propose a novel D-type flip-flop structure. The presented structure boasts a remarkably low power consumption, showcasing a reduction exceeding 61% compared to other existing structures. Furthermore, the proposed circuit incorporates a reduced number of transistors, resulting in a reduced footprint. Importantly, this circuit exhibits negligible static power consumption in generating intermediate values, rendering it robust against process variations.  Overall, the proposed circuits demonstrate a 29.7% increase in delay compared to the compared structures. However, they showcase a 96.1% reduction in power-delay product (PDP) compared to the other structures. The number of transistors is also 8.3% less than other structures. Additionally, their figure of merits (FOM) are 19.7% better than the best-compared circuit, underscoring its advantages in power efficiency, chip area, and performance. 83 93 2023/01/092023/04/042023/05/312023/06/122023/07/022023/08/152023/09/082023/09/18 1402/6/27 2024/03/182024/03/242024/02/282024/02/162024/03/072024/02/282024/02/182024/03/19 1402/12/29 Amirhossein Salimi Amirhossein Salimi Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Iran salimiamir36@yahoo.com Behzad Ebrahimi Behzad Ebrahimi Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Iran behzad.ebrahimi@srbiau.ac.ir Massoud Dousti Massoud Dousti Department of Electrical and Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Iran m_dousti@srbiau.ac.ir CNTFET Flip-Flop High Performance Low Power Multi-Valued [1] N. H. Weste and D. Harris, "CMOS VLSI design: a circuits and systems perspective," Pearson Education India, 2015.##[2] S. Iijima, "Helical microtubules of graphitic carbon," Nature, vol. 354, no. 6348, pp. 56, 1991.##[3] K. S. Novoselov et al., "Electric field effect in atomically thin carbon films," Science, vol. 306, no. 5696, pp. 666-669, 2004.##[4] S. J. Tans, A. RM Verschueren, and C. Dekker, "Room-temperature transistor based on a single carbon nanotube," Nature, vol. 393, no. 6680, p. 49, 1998.##[5] A. Bachtold et al., "Logic circuits with carbon nanotube transistors," Science, vol. 294, no. 5545, pp. 1317-1320, 2001.##[6] S. Kundu, S. P. Mohanty, and N. Ranganathan, "Guest editorial-Design methodologies for nanoelectronic digital and analogue circuits," IET Circuits, Devices & Systems, vol. 7, no. 5, pp. 221-222, 2013.##[7] A. Raychowdhury and K. Roy, "Carbon-nanotube-based voltage-mode multiple-valued logic design," IEEE Transactions on Nanotechnology, vol. 4, no. 2, pp. 168-179, 2005.##[8] M. H. Moaiyeri, A. Doostaregan, and K. Navi, "Design of energy-efficient and robust ternary circuits for nanotechnology," IET Circuits, Devices & Systems, vol. 5, no. 4, pp. 285-296, 2011.##[9] S. Lin, Y.-B. Kim, and F. Lombardi, "CNTFET-based design of ternary logic gates and arithmetic circuits," IEEE Transactions on Nanotechnology, vol. 10, no. 2, pp. 217-225, 2009.##[10] K. Navi et al., "High speed capacitor-inverter based carbon nanotube full adder," Nanoscale Research Letters, vol. 5, no. 5, p. 859, 2010.##[11] M. H. Moaiyeri, R. F. Mirzaee, K. Navi, and O. Hashemipour, "Efficient CNTFET-based ternary full adder cells for nanoelectronics," Nano-Micro Letters, vol. 3, no. 1, pp. 43-50, 2011.##[12] P. Keshavarzian and R. Sarikhani, "A novel CNTFET-based ternary full adder," Circuits, Systems, and Signal Processing, vol. 33, no. 3, pp. 665-679, 2014.##[13] K. Sridharan, S. Gurindagunta, and V. Pudi, "Efficient multiternary digit adder design in CNTFET technology," IEEE Transactions on Nanotechnology, vol. 12, no. 3, pp. 283-287, 2013.##[14] M. Jasemi, R. F. Mirzaee, K. Navi, and N. Bagherzadeh, "Voltage mirror circuit by carbon nanotube field effect transistors for mirroring dynamic random access memories in multiple-valued logic and fuzzy logic," IET Circuits, Devices & Systems, vol. 9, no. 5, pp. 343-352, 2015.##[15] D. M. Miller and M. A. Thornton, "Multiple valued logic: Concepts and representations," Synthesis Lectures on Digital Circuits and Systems, vol. 2, no. 1, pp. 1-127, 2007. ##[16] P. Behrooz, "Computer arithmetic: Algorithms and hardware designs," Oxford University Press, pp. 512583-512585, 2000.##[17] J. Deng and H.-S. P. Wong, "A compact SPICE model for carbon-nanotube field-effect transistors including nonidealities and its application—Part I: Model of the intrinsic channel region," IEEE Transactions on Electron Devices, vol. 54, no. 12, pp. 3186-3194, 2007.##[18] A. P. Dhande and V. T. Ingole, "Design and implementation of 2 bit ternary ALU slice," in Proc. Int. Conf. IEEE-Sci. Electron., Technol. Inf. Telecommun, pp. 17-21, 2005.##[19] S. L. Murotiya and A. Gupta, "Design of high speed ternary full adder and three-input XOR circuits using CNTFETs," in 2015 28th International Conference on VLSI Design, pp. 292-297, IEEE, 2015.##[20] S. Karmakar, J. A. Chandy, and F. C. Jain, "Design of ternary logic combinational circuits based on quantum dot gate FETs," IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 21, no. 5, pp. 793-806, 2012.##[21] S. Cotofana, C. Lageweg, and S. Vassiliadis, "Addition related arithmetic operations via controlled transport of charge," IEEE Transactions on Computers, vol. 54, no. 3, pp. 243-256, 2005.##[22] B. Srinivasu and K. Sridharan, "Carbon nanotube FET-based low-delay and low-power multi-digit adder designs," IET Circuits, Devices & Systems, vol. 11, no. 4, pp. 352-364, 2016.##[23] M. Bansal, H. Singh, and G. Sharma, "A taxonomical review of multiplexer designs for electronic circuits & devices," Journal of Electronics, vol. 3, no. 02, pp. 77-88, 2021.##[24] N. Muranaka and S. Imanishi, "Construction of up‐down‐type and shift‐register‐type counters using ternary flip‐flop circuits," Systems and Computers in Japan, vol. 16, no. 5, pp. 97-105, 1985.##[25] S. Tabrizchi et al., "Energy-efficient ternary multipliers using CNT transistors," Electronics, vol. 9, no. 4, p. 643, 2020.##[26] S. Lin, Y.-B. Kim, and F. Lombardi, "CNTFET-based design of ternary logic gates and arithmetic circuits," IEEE Transactions on Nanotechnology, vol. 10, no. 2, pp. 217-225, 2009.##[27] M. H. Moaiyeri, A. Doostaregan, and K. Navi, "Design of energy-efficient and robust ternary circuits for nanotechnology," IET Circuits, Devices & Systems, vol. 5, no. 4, pp. 285-296, 2011.##[28] H. Samadi, A. Shahhoseini, and F. Aghaei-liavali, "A new method on designing and simulating CNTFET_based ternary gates and arithmetic circuits," Microelectronics Journal, vol. 63, pp. 41-48, 2017.##[29] S. Tabrizchi et al., "Novel CNFET ternary circuit techniques for high‐performance and energy‐efficient design," IET Circuits, Devices & Systems, vol. 13, no. 2, pp. 193-202, 2019.##[30] K. Rahbari and S. A. Hosseini, "Novel ternary D-flip-flap-flop and counter based on successor and predecessor in nanotechnology," AEU-International Journal of Electronics and Communications, vol. 109, pp. 107-120, 2019.##[31] Y. Kang et al., "Design of ternary pulsed reversible counter based on CNFET," in 2017 IEEE 12th International Conference on ASIC (ASICON), pp. 375-378, IEEE, 2017.##[32] J. Deng and H.-S. P. Wong, "A compact SPICE model for carbon-nanotube field-effect transistors including nonidealities and its application—Part I: Model of the intrinsic channel region," IEEE Transactions on Electron Devices, vol. 54, no. 12, pp. 3186-3194, 2007.##[33] T. Sharma and D. Sharma, "Design of ternary flip-flop cells using Maximum/Minimum logic operators in carbon nanotube technology," in 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), pp. 1693-1697, IEEE, 2022.##[34] M. H. Moaiyeri, M. Nasiri, and N. Khastoo, "An efficient ternary serial adder based on carbon nanotube FETs," Engineering Science and Technology, an International Journal, vol. 19, no. 1, pp. 271-278, 2016.##[35] K. Rahbari and S. A. 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FirePSOSA: A Hybrid Metaheuristic Approach for Enhanced Segmentation of Maize Leaves 2 2 The potential adverse effects of maize leaf diseases on agricultural productivity highlight the significance of precise disease diagnosis using effective leaf segmentation techniques. In order to improve maize leaf segmentation, especially for maize leaf disease detection, a hybrid optimization method is proposed in this paper. The proposed method provides better segmentation accuracy and outperforms traditional approaches by combining enhanced Particle Swarm Optimisation (PSO) with Firefly algorithm (FFA). Extensive tests on images of maize leaves taken from the Plant Village dataset are used to show the algorithm's superiority. Experimental results show a considerable decrease in Hausdorff distances, indicating better segmentation accuracy than conventional methods. The proposed method also performs better than expected in terms of Jaccard and Dice coefficients, which measure the overlap and similarity between segmented sections. The proposed hybrid optimization method significantly contributes to agricultural research and indicates that the method may be helpful in real scenarios.  The performance of proposed method is compared with existing techniques like K-Mean, OTSU, Canny, FuzzyOTSU, PSO and Firefly. The overall performance of the proposed method is satisfactory. 94 110 2023/01/092023/04/042023/05/312023/06/122023/07/022023/08/152023/09/082023/09/182023/10/11 1402/7/19 2024/03/182024/03/242024/02/282024/02/162024/03/072024/02/282024/02/182024/03/192024/03/19 1402/12/29 Priyanka Handa Priyanka Handa CDLSIET Panniwala Mota India priyankahanda19@gmail.com Balkrishan Jindal Balkrishan Jindal YDoE, University Guru Kashi Campus, Punjabi University India balkrishan@pbi.ac.in Maize leaf Optimization PSO SA Firefly Segmentation. [1] A. Negi, K. Kumar, and P. Chauhan, “Deep S,” Agricultural Informatics, pp. 117–129, Mar. 2021, doi: 10.1002/9781119769231.ch6. ##[2] F. Rajeena. P. P, S. U. Aswathy, M. A. Moustafa, and M. a. S. Ali, “Detecting plant disease in corn leaf using efficientnet architecture—an analytical approach,” Electronics, vol. 12, no. 8, p. 1938, Apr. 2023 ##[3] A. Benyam, T. Soma, and E. D. G. Fraser, “Digital agricultural technologies for food loss and waste prevention and reduction: Global trends, adoption opportunities and barriers,” Journal of Cleaner Production, vol. 323, p. 129099, Nov. 2021 ##[4] L. F. P. De Oliveira, A. P. Moreira, and M. F. Silva, “Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges ahead,” Robotics, vol. 10, no. 2, p. 52, Mar. 2021.##[5] D. . J. M. Bonifacio, A. M. I. E. Pascual, M. V. C. Caya, and J. C. Fausto, “Determination of Common Maize (Zea mays) Disease Detection using Gray-Level Segmentation and Edge-Detection Technique,” 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, Dec. 2020. ##[6] J. A. Wani, S. Sharma, M. Muzamil, S. Ahmed, S. Sharma, and S. Singh, "Machine Learning and Deep Learning Based Computational Techniques in Automatic Agricultural Diseases Detection: Methodologies, Applications, and Challenges," Arch. Comput. Methods Eng., vol. 29, no. 1, pp. 641–677, Jan. 2022.##[7] P. Akulwar, “A Recommended System for Crop Disease Detection and Yield Prediction Using Machine Learning Approach,” Recommender System With Machine Learning and Artificial Intelligence, Wiley, pp. 141–163, Jun. 2020##[8] G. Dhingra, V. Kumar, and H. D. Joshi, “Study of digital image processing techniques for leaf disease detection and classification,” Multimedia Tools and Applications, vol. 77, no. 15, pp. 19951–20000, Nov. 2017.##[9] S. Bondre and D. Patil, “Recent advances in agricultural disease image recognition technologies: A review,” Concurrency and Computation: Practice and Experience, vol. 35, no. 9, Feb. 2023.##[10] S. Kalaivani, S. P. Shantharajah, and T. Padma, “Agricultural leaf blight disease segmentation using indices based histogram intensity segmentation approach,” Multimedia Tools and Applications, vol. 79, no. 13–14, pp. 9145–9159, Jan. 2019.##[11] R. Saleem, J. H. Shah, M. Sharif, M. Yasmin, H.-S. Yong, and J. Cha, “Mango leaf disease recognition and classification using novel segmentation and vein pattern technique,” Applied Sciences, vol. 11, no. 24, p. 11901, Dec. 2021.##[12] A. I. Khan, S. M. K. Quadri, S. Banday, and J. L. Shah, “Deep diagnosis: A real-time apple leaf disease detection system based on deep learning,” Computers and Electronics in Agriculture, vol. 198, p. 107093, Jul. 2022. ##[13] B. G. K. Madhavi, A. Bhujel, N. E. Kim, and H. T. Kim, “Measurement of overlapping leaf area of ice plants using digital image processing technique,” Agriculture, vol. 12, no. 9, p. 1321, Aug. 2022.##[14] T. Xiao, H. Liu, and Y. Cheng, “Corn Disease Identification Based on improved GBDT Method,” 2019 6th International Conference on Information Science and Control Engineering (ICISCE), IEEE, Dec. 2019. ##[15] S. E. Sukmana and F. Z. Rahmanti, “Blight segmentation on corn crop leaf using connected component extraction and CIELAB color space transformation,” 2017 International Seminar on Application for Technology of Information and Communication (iSemantic), IEEE, Oct. 2017.##[16] M. Sibiya and M. Sumbwanyambe, “An algorithm for severity estimation of plant leaf diseases by the use of colour threshold image segmentation and fuzzy logic Inference: a proposed algorithm to update a ‘Leaf doctor’ application,” AgriEngineering, vol. 1, no. 2, pp. 205–219, May 2019.##[17] S. Mousavi, Z. Hanifeloo, P. Sumari, and M. R. M. Arshad, "Enhancing the Diagnosis of Corn Pests using Gabor Wavelet Features and SVM Classification.," Semant. Sch., 2016.##[18] P. Dayang and A. S. K. Meli, “Evaluation of image segmentation algorithms for plant disease detection,” International Journal of Image, Graphics and Signal Processing, vol. 13, no. 5, pp. 14–26, Oct. 2021. ##[19] P. Panchal, V. C. Raman, and S. Mantri, “Plant Diseases Detection and Classification using Machine Learning Models,” 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS), IEEE, Dec. 2019.##[20] X. Xiong et al., “Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization,” Plant Methods, vol. 13, no. 1, Nov. 2017.##[21] L. G. Divyanth, A. Ahmad, and D. Saraswat, “A two-stage deep-learning based segmentation model for crop disease quantification based on corn field imagery,” Smart Agricultural Technology, vol. 3, p. 100108, Feb. 2023.##[22] S.-Q. Pan et al., “Intelligent diagnosis of northern corn leaf blight with deep learning model,” Journal of Integrative Agriculture, vol. 21, no. 4, pp. 1094–1105, Apr. 2022.##[23] Y. Xiong, L. Liang, L. Wang, J. She, and M. Wu, “Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset,” Computers and Electronics in Agriculture, vol. 177, p. 105712, Oct. 2020.##[24] L. Deng et al., “Application of agricultural insect pest detection and control map based on image processing analysis,” Journal of Intelligent and Fuzzy Systems, vol. 38, no. 1, pp. 379–389, Jan. 2020.##[25] J. Tian, Y. Zhang, Y. Wang, C. Wang, S. Zhang, and T. Ren, “A Method of Corn Disease Identification Based on Convolutional Neural Network,” 12th International Symposium on Computational Intelligence and Design (ISCID), IEEE, Dec. 2019.##[26] J. G. A. Barbedo, “A novel algorithm for semi-automatic segmentation of plant leaf disease symptoms using digital image processing,” Tropical Plant Pathology, vol. 41, no. 4, pp. 210–224, Jun. 2016.##[27] V. Singh, “Sunflower leaf diseases detection using image segmentation based on particle swarm optimization,” Artificial Intelligence in Agriculture, vol. 3, pp. 62–68, Sep. 2019.##[28] M. Yogeshwari and D. G. Thailambal, "Automatic segmentation of plant leaf disease using improved fast fuzzy c means clustering and adaptive otsu thresholding," Semant. Sch., 2020.##[29] M. Jayanthi and D. R. Shashikumar, “Leaf disease segmentation from agricultural images via hybridization of active contour model and OFA,” Journal of Intelligent Systems, vol. 29, no. 1, pp. 35–52, Nov. 2017.##[30] I. Fister, X. Yang, and D. Fister, “Firefly Algorithm: A Brief review of the expanding literature,” in Springer eBooks, pp. 347–360, 2013.##[31] C. Kaushal, K. Kaushal, and A. Singla, “Firefly optimization-based segmentation technique to analyse medical images of breast cancer,” International Journal of Computer Mathematics, vol. 98, no. 7, pp. 1293–1308, Sep. 2020.##[32] N. K. Jain, U. Nangia, and J. Jain, “A review of particle swarm optimization,” Journal of Institution of Engineers (India) Series B, vol. 99, no. 4, pp. 407–411, Mar. 2018.##[33] E. Mirsadeghi and S. Khodayifar, “Hybridizing particle swarm optimization with simulated annealing and differential evolution,” Cluster Computing, vol. 24, no. 2, pp. 1135–1163, Sep. 2020.##[34] D. P. Hughes and M. Salathe, "An open access repository of images on plant health to enable the development of mobile disease diagnostics," Nov. 2015, [Online]. Available: http://arxiv.org/abs/1511.08060##[35] V. Rajinikanth and M. S. Couceiro, “RGB histogram based color image segmentation using Firefly algorithm,” Procedia Computer Science, vol. 46, pp. 1449–1457, Jan. 2015.##[36] D. Karimi and S. E. Salcudean, “Reducing the Hausdorff distance in medical image segmentation with convolutional neural networks,” IEEE Transactions on Medical Imaging, vol. 39, no. 2, pp. 499–513, Feb. 2020. ##[37] M. A. Patil and M. Manur, “Enhanced radial basis function neural network for tomato plant disease leaf image segmentation,” Ecological Informatics, vol. 70, p. 101752, Sep. 2022.## ##

Comparative Analysis of Polar Coded F-OFDM and UFMC 5G NR Waveforms 2 2 Fifth Generation-New Radio (5G-NR) is an advanced air interface defined to fulfil diverse services with ubiquitous coverage in next generation Wireless networks. The waveform is the crucial part of air interface that must have good spectral confinement and low peak-to-average power ratio (PAPR). Orthogonal Frequency Division Multiplexing (OFDM) is a widely used air interface in Fourth Generation Long Term Evolution (4G-LTE) system. But OFDM suffers from high PAPR, Carrier Frequency offset (CFO), and loss of spectral efficiency due to insertion of cyclic prefix. So, the high dense networks with heterogeneous traffic in the 5G requires new multicarrier waveform. In the proposed work, waveforms based on sub-band filtering are considered due to more flexibility and shorter filter length as compared to the sub-carrier-based filtering waveforms. Two major 5G waveform candidates Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM) and Universal Frequency Division Multiplexing (UFMC) are proposed in the system design. Channel coding is the inherent part of air interface for enhancing the error performance. New error correcting channel codes introduced in NR to support variable information block length and flexible codeword size. The capacity achieving Polar codes is the highlight of this paper adopted for control channels. 5G NR air interface using new modulation waveform along with the polar coding can be an effective way to enhance error performance. This paper presents comparative analysis of comprehensive systems Polar coded F-OFDM (PC-F-OFDM) and Polar coded UFMC (PC-UFMC) in massive MIMO scenario. Simulation results indicate that the proposed PC-F-OFDM systems significantly outperform the PC-UFMC systems in AWGN channel. But in massive MIMO setup BER performance of PC-UFMC is better than PC-F-OFDM system. 111 121 2023/01/092023/04/042023/05/312023/06/122023/07/022023/08/152023/09/082023/09/182023/10/112023/12/06 1402/9/15 2024/03/182024/03/242024/02/282024/02/162024/03/072024/02/282024/02/182024/03/192024/03/192024/03/13 1402/12/23 Smita Jolania Smita Jolania Institute of Engineering and Technology, DAVV India sprajapati2911@gmail.com Ravi Sindal Ravi Sindal Institute of Engineering and Technology India rsindal@ietdavv.edu.in New Radio F-OFDM UFMC Polar Codes Massive MIMO [1] ITU-R, “IMT Vision - Framework and overall objectives of the future development of IMT for 2020 and beyond,” Technical Report M.2083-0, Sept. 2015.##[2] Zhang, X. et al. (2016) ‘On the waveform for 5G’, IEEE Communications Magazine, 54(11), pp. 74–80. doi:10.1109/mcom.2016.1600337cm##[3] Qualcomm Inc., “Waveform candidates,” 3GPP Standard Contribution (R1-162199), Busan, Korea, Apr. 11-15, 2016.##[4] T. Hwang, C. Yang, G. Wu, S. Li, and G. Ye Li, "OFDM and Its Wireless Applications: A Survey," in IEEE Transactions on Vehicular Technology, vol. 58, no. 4, pp. 1673-1694, May 2009, doi: 10.1109/TVT.2008.2004555.##[5] R. Nissel, S. Schwarz, and M. Rupp, "Filter Bank Multicarrier Modulation Schemes for Future Mobile Communications," in IEEE Journal on Selected Areas in Communications, vol. 35, no. 8, pp. 1768-1782, Aug. 2017, doi: 10.1109/JSAC.2017.2710022.##[6] G. Fettweis, M. Krondorf and S. Bittner, "GFDM - Generalized Frequency Division Multiplexing," VTC Spring 2009 - IEEE 69th Vehicular Technology Conference, Barcelona, Spain, 2009, pp. 1-4, doi: 10.1109/VETECS.2009.5073571.##[7] F. Schaich, T. Wild and Y. Chen, "Waveform Contenders for 5G - Suitability for Short Packet and Low Latency Transmissions," 2014 IEEE 79th Vehicular Technology Conference (VTC Spring), Seoul, Korea (South), 2014, pp. 1-5, doi: 10.1109/VTCSpring.2014.7023145. ##[8] X. Zhang, M. Jia, L. Chen, J. Ma, and J. Qiu, "Filtered-OFDM - Enabler for Flexible Waveform in the 5th Generation Cellular Networks," 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA, USA, 2015, pp. 1-6, doi: 10.1109/GLOCOM.2015.7417854. ##[9] Demir, Ali Fatih, Mohamed Elkourdi, Mostafa Ibrahim and Hüseyin Arslan. “Waveform Design for 5G and Beyond.” 5G Networks: Fundamental Requirements, Enabling Technologies, and Operations Management (2018): n. pag. ##[10] A. Hammoodi, L. Audah and M. A. Taher, "Green Coexistence for 5G Waveform Candidates: A Review," in IEEE Access, vol. 7, pp. 10103-10126, 2019, doi: 10.1109/ACCESS.2019.2891312.##[11] Van Eeckhaute, M., Bourdoux, A., De ncker, P. et al. Performance of emerging multi-carrier waveforms for 5G asynchronous communications. J Wireless Com Network 2017, 29 (2017). https://doi.org/10.1186/s13638-017-0812-8##[12] Y. 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Voltage Error Decreasing of Power Systems by State Estimation Strategy in the Presence of DG Units Using the Improved Taguchi Method and Phasor Measurement Unit 2 2 In recent decades, because of the rapid population growth of the world, considerable changes in climate, the reduction of fossil fuel sources to consume the traditional power plants and their high depreciation, and the increase in fuel prices.  Due to the increased penetration of DG units which have a random nature into the power system, the ordinary equations of power flow must be changed. For the power system to operate in a stable condition estimating future demand and calculating the important and operational indexes such as losses of the power system is an important duty that must be done precisely and rapidly. In this paper, the Improved Taguchi method and phasor measurement unit are used to model the uncertainties of DGs and estimate the error of voltage, respectively. The results show that the magnitude error and the angle error of voltage are decreased using PMU. The applied optimal power flow and state estimations are analyzed and verified using standard IEEE 30-bus and 14-bus test power systems by MATLAB, and MINITAB softwares. The Made Strides Taguchi strategy appears to have modeled the DG units precisely and successfully, and using the PMU, the mistake of the point and greatness estimation is exceptionally moot. The values that were evaluated are very close to the values that were done by the Newton-Raphson stack stream. 122 132 2023/01/092023/04/042023/05/312023/06/122023/07/022023/08/152023/09/082023/09/182023/10/112023/12/062023/12/13 1402/9/22 2024/03/182024/03/242024/02/282024/02/162024/03/072024/02/282024/02/182024/03/192024/03/192024/03/132024/02/18 1402/11/29 Majid Najjarpour Majid Najjarpour Electrical and Computer Engineering, Faculty, Urmia University, Urmia, Iran. Iran st_m.najjarpour@urmia.ac.ir Behrouz Tousi Behrouz Tousi Electrical and Computer Engineering, Faculty, Urmia University, Urmia, Iran. Iran b.tousi@urmia.ac.ir Shahaboddin Yazdandoust Moghanlou Shahaboddin Yazdandoust Moghanlou Faculty of Medicine, Tabriz university of Medical sciences, Tabriz, Iran Iran Shahabyd95@gmail.com Distributed Generation Taguchi Method Orthogonal arrays Optimal Power Flow Uncertainty State Estimation Phasor Measurement Unit. [1] P. S. Kundur and O. P. Malik, Power system stability and control. McGraw-Hill Education, 2022.##[2] Z. Huang, B. Fang, and J. Deng, "Multi-objective optimization strategy for distribution network considering V2G-enabled electric vehicles in building integrated energy system," Protection and Control of Modern Power Systems, vol. 5, pp. 1-8, 2020.##[3] A. García-Olivares, J. Solé, and O. Osychenko, "Transportation in a 100% renewable energy system," Energy Conversion and Management, vol. 158, pp. 266-285, 2018.##[4] H. M. 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Liu, "Random fuzzy power flow of distribution network with uncertain wind turbine, PV generation, and load based on random fuzzy theory," IET Renewable Power Generation, vol. 12, no. 10, pp. 1180-1188, 2018.##[23] A. Soroudi and M. Ehsan, "A possibilistic–probabilistic tool for evaluating the impact of stochastic renewable and controllable power generation on energy losses in distribution networks—A case study," Renewable and Sustainable Energy Reviews, vol. 15, no. 1, pp. 794-800, 2011.##[24] M. Aien, A. Hajebrahimi, and M. Fotuhi-Firuzabad, "A comprehensive review on uncertainty modeling techniques in power system studies," Renewable and Sustainable Energy Reviews, vol. 57, pp. 1077-1089, 2016.##[25] H. Yao et al., "Possibilistic evaluation of photovoltaic hosting capacity on distribution networks under uncertain environment," Applied Energy, vol. 324, p. 119681, 2022.##[26] A. R. Jordehi, "How to deal with uncertainties in electric power systems? A review," Renewable and sustainable energy reviews, vol. 96, pp. 145-155, 2018.##[27] S. Pineda and A. Conejo, "Scenario reduction for risk-averse electricity trading," IET generation, transmission & distribution, vol. 4, no. 6, pp. 694-705, 2010.##[28] A. Soroudi and M. Afrasiab, "Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty," IET renewable power generation, vol. 6, no. 2, pp. 67-78, 2012.##[29] Y. Ben-Haim, Info-gap decision theory: decisions under severe uncertainty. Elsevier, 2006.##[30] A. Rabiee, A. Soroudi, and A. Keane, "Information gap decision theory based OPF with HVDC connected wind farms," IEEE Transactions on Power Systems, vol. 30, no. 6, pp. 3396-3406, 2014.##[31] M. Nazari-Heris and B. Mohammadi-Ivatloo, "Application of robust optimization method to power system problems. Classical and recent aspects of power system optimization," Academic Press, Cambridge, US, vol. 2, pp. 19-32, 2018.##[32] A. Ben-Tal, L. El Ghaoui, and A. Nemirovski, Robust optimization. Princeton university press, 2009.##[33] R. E. Moore, R. B. Kearfott, and M. J. Cloud, Introduction to interval analysis. SIAM, 2009.##[34] P. Zhang, W. Li, and S. Wang, "Reliability-oriented distribution network reconfiguration considering uncertainties of data by interval analysis," International Journal of Electrical Power & Energy Systems, vol. 34, no. 1, pp. 138-144, 2012.##[35] J. Zhao et al., "Power system dynamic state estimation: Motivations, definitions, methodologies, and future work," IEEE Transactions on Power Systems, vol. 34, no. 4, pp. 3188-3198, 2019.##[36] A. H. Abolmasoumi, A. Farahani, and L. Mili, "Robust Particle Filter Design with an Application to Power System State Estimation," IEEE Transactions on Power Systems, 2023.##[37] G. Cheng, Y. Lin, A. Abur, A. Gómez-Expósito, and W. 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Performance Analysis of the Efficiency of Cooperative Communication Systems Utilizing Radio Frequency Energy Harvesting 2 2 The progress of 5G networks is propelled by wireless technology, specifically mobile internet and smart devices. This article provides an in-depth analysis of the fundamental elements of 5G technology, encompassing the advancement of cellular networks, simultaneous transmission capabilities, energy efficiency enhancements, and the implementation of cooperative communication. This study examines the application of simultaneous wireless information and power transfer (SWIPT) in cooperative device-to-devices (D2D) communication. Specifically, it investigates relay selection using decode-forward (DF) protocols and considers the issue of self-interference. Radio frequency based energy harvesting (RF-EH) is proposed to address power limitations in device-to-device (D2D) communication. This article describes the development of this technology and suggests a system architecture that employs time-switching relaying (TSR) techniques to enhance the power efficiency of base stations. This research aims to assess data transfer efficiency in two-way cooperative communication systems by incorporating many technologies. 133 143 2023/01/092023/04/042023/05/312023/06/122023/07/022023/08/152023/09/082023/09/182023/10/112023/12/062023/12/132023/12/21 1402/9/30 2024/03/182024/03/242024/02/282024/02/162024/03/072024/02/282024/02/182024/03/192024/03/192024/03/132024/02/182024/03/23 1403/1/4 Tasqiatul Qulbi Kamila Huda Tasqiatul Qulbi Kamila Huda Politeknik Elektronika Negeri Surabaya Indonesia tasqiatulqulbi@te.student.pens.ac.id I Gede Puja Astawa I Gede Puja Astawa Politeknik Elektronika Negeri Surabaya Indonesia puja@pens.ac.id Yoedy Moegiharto Yoedy Moegiharto Politeknik Elektronika Negeri Surabaya Indonesia ymoegiharto@pens.ac.id Mohamad Ridwan Mohamad Ridwan Politeknik Elektronika Negeri Surabaya Indonesia ridwan@pens.ac.id Budi Aswoyo Budi Aswoyo Politeknik Elektronika Negeri Surabaya Indonesia budias@eepis-its.id Anang Budikarso Anang Budikarso Politeknik Elektronika Negeri Surabaya Indonesia anang_bk@pens.ac.id Ida Anisah Ida Anisah Politeknik Elektronika Negeri Surabaya Indonesia ida@pens.ac.id Faridatun Nadziroh Faridatun Nadziroh Politeknik Elektronika Negeri Surabaya Indonesia faridatun@pens.ac.id D2D Communication DF Protocol RF-EH with TSR Throughput. 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Stochastic Scheduling of Renewable-Based Energy Systems Considering Power-to-Hydrogen and Hydrogen-to-Power Units 2 2 This paper proposes a stochastic optimization problem for local integrated hydrogen-power energy systems. In the proposed model, the integrated system tries to reduce the day-ahead operation costs using dispatchable resources, renewable energy resources, battery energy storage systems, demand response programs, and energy trading with the upstream network. Also, the integrated system is able to transact electricity with the upstream network to get more benefits. When the generation of renewable resources is high, the integrated system can convert the surplus electricity to hydrogen by power-to-gas units. The generated hydrogen can be sold to different industries or stored in the hydrogen tank storage. During peak hours, the stored hydrogen can be imported into the gas-to-power unit to generate the required electricity. The sector coupling between electricity and hydrogen provides more flexibility for integrated systems and is an effective solution to control the uncertainty of renewable energy resources in order to increase the power and energy flexibilities. The simulation results show that the proposed sector coupling provides the opportunity for electricity and hydrogen trading for integrated system. The benefit of the integrated system by electricity and hydrogen trading with the upstream network and different industries are $ 88.39, and $ 6846, respectively. 144 154 2023/01/092023/04/042023/05/312023/06/122023/07/022023/08/152023/09/082023/09/182023/10/112023/12/062023/12/132023/12/212023/12/30 1402/10/9 2024/03/182024/03/242024/02/282024/02/162024/03/072024/02/282024/02/182024/03/192024/03/192024/03/132024/02/182024/03/232024/03/04 1402/12/14 Hamid Karimi Hamid Karimi Qom university of technology Iran h_karimi@elec.iust.ac.ir Micro-energy Systems Hydrogen-to-power Power-to-hydrogen Sector Coupling Demand Response Programs [1] Behbahani, Milad Rahimipour, Alireza Jalilian, Alireza Bahmanyar, and Damien Ernst. "Comprehensive Review on Static and Dynamic Distribution Network Reconfiguration Methodologies." IEEE Access, vol. 12, 2024.##[2] Behbahani, Milad Rahimipour, and Alireza Jalilian. 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