Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 13 1 2017 3 1 A Bilevel Scheduling Approach for Modeling Energy Transaction of Virtual Power Plants in Distribution Networks 1 9 EN F. Nazari Department of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran. farshidnazari86@gmail.com N A. Zangeneh Department of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran. a.zangeneh@srttu.edu Y A. Shayegan-Rad Department of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran. alishayegan8@gmail.com N 10.22068/IJEEE.13.1.1 By increasing the use of distributed generation (DG) in the distribution network operation, an entity called virtual power plant (VPP) has been introduced to control, dispatch and aggregate the generation of DGs, enabling them to participate either in the electricity market or the distribution network operation. The participation of VPPs in the electricity market has made challenges to fairly allocate payments and benefits between VPPs and distribution network operator (DNO). This paper presents a bilevel scheduling approach to model the energy transaction between VPPs and DNO.  The upper level corresponds to the decision making of VPPs which bid their long- term contract prices so that their own profits are maximized and the lower level represents the DNO decision making to supply electricity demand of the network by minimizing its overall cost. The proposed bilevel scheduling approach is transformed to a single level optimizing problem using its Karush-Kuhn-Tucker (KKT) optimality conditions. Several scenarios are applied to scrutinize the effectiveness and usefulness of the proposed model.  Bilevel Scheduling Approach,Distributed Generation,Distribution Network Operator,Karush-Kuhn-Tucker,Virtual Power Plant, http://ijeee.iust.ac.ir/article-1-1038-en.html http://ijeee.iust.ac.ir/article-1-1038-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 13 1 2017 3 1 A Control Strategy for Flywheel Energy Storage System for Frequency Stability Improvement in Islanded Microgrid 10 21 EN A. A. Khodadoost Arani Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran. a.a.khodadoost@aut.ac.ir N B. Zaker Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran. zaker.behrooz@aut.ac.ir N G. B. Gharehpetian Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran. grptian@aut.ac.ir Y 10.22068/IJEEE.13.1.2 The Micro-Grid (MG) stability is a significant issue that must be maintained in all operational modes. Usually, two control strategies can be applied to MG; V/f control and PQ control strategies. MGs with V/f control strategy should have some Distributed Generators (DGs) which have fast responses versus load changes. The Flywheel Energy Storage System (FESS) has this characteristic. The FESS, which converts the mechanical energy to electrical form, can generate electrical power or absorb the additional power in power systems or MGs. In this paper, the FESS structure modeled in detail and two control strategies (V/f and PQ control) are applied for this application. In addition, in order to improve the MG frequency and voltage stability, two complementary controllers are proposed for the V/f control strategy; conventional PI and Fuzzy Controllers. A typical low voltage network, including FESS is simulated for four distinct scenarios in the MATLAB/ Simulink environment. It is shown that fuzzy controller has better performance than conventional PI controller in islanded microgrid. Flywheel Energy Storage System (FESS),Fuzzy Controller,Microgrid (MG),Voltage and Frequency Stability,Stored Energy, http://ijeee.iust.ac.ir/article-1-1006-en.html http://ijeee.iust.ac.ir/article-1-1006-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 13 1 2017 3 1 Optimal Placement and Sizing of Distributed Generations in Unbalanced Distribution Networks Considering Load Models and Uncertainties 22 31 EN F. Amini Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran. f_amini@sut.ac.ir N R. Kazemzadeh Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran. r.kazemzadeh@sut.ac.ir Y 10.22068/IJEEE.13.1.3 Development of distributed generations’ technology, trends in the use of these sources to improve some of the problems such as high losses, low reliability, low power quality and high costs in distributed networks. Choose the correct location to install and proper capacity of these sources, such as important things that must be considered in their use. Since distribution networks are actually unbalanced and asymmetric consumption loads are different, so in this paper with optimal placement and sizing of distributed generation sources that dependent on the load model and type of load connection and the uncertainties which caused by the generated power of wind turbines and solar panels, the positive effects of these sources have been examined on unbalanced distribution network. Hence with linear three-phase unbalanced load flow method and IPSO algorithm, allocation of distributed generation sources in IEEE standard of 37 bus unbalanced network have been done. Obtained results show improvement of voltage profile in each phase and reduction of network power losses and buses’ voltage unbalance factor.  Distributed Generations’ Uncertainty,Linear Three Phase Unbalanced Load Flow,Load Model,Unbalanced Distribution Network, http://ijeee.iust.ac.ir/article-1-1024-en.html http://ijeee.iust.ac.ir/article-1-1024-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 13 1 2017 3 1 Locating of Series FACTS Devices for Multi-Objective Congestion Management Using Components of Nodal Prices 32 46 EN A. R. Moradi Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. alireza.moradi@semnan.ac.ir N Y. Alinejad-Beromi Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. yalinejad@semnan.ac.ir Y K. Kiani Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. kourosh.kiani@semnan.ac.ir N 10.22068/IJEEE.13.1.4 Congestion and overloading for lines are the main problems in the exploitation of power grids. The consequences of these problems in deregulated systems can be mentioned as sudden jumps in prices in some parts of the power system, lead to an increase in market power and reduction of competition in it. FACTS devices are efficient, powerful and economical tools in controlling power flows through transmission lines that play a fundamental role in congestion management. However, after removing congestion, power systems due to targeting security restrictions may be managed with a lower voltage or transient stability rather than before removing. Thus, power system stability should be considered within the construction of congestion management. In this paper, a multi-objective structure is presented for congestion management that simultaneously optimizes goals such as total operating cost, voltage and transient security. In order to achieve the desired goals, locating and sizing of series FACTS devices are done with using components of nodal prices and the newly developed grey wolf optimizer (GWO) algorithm, respectively. In order to evaluate reliability of mentioned approaches, a simulation is done on the 39-bus New England network. Congestion Management,Electricity Markets,FACTS Devices,Multi-Objective Optimization,Power System Stability Margin, http://ijeee.iust.ac.ir/article-1-1025-en.html http://ijeee.iust.ac.ir/article-1-1025-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 13 1 2017 3 1 Perspective of MAS in Power System via a Fuzzy Framework 47 56 EN S. Razini Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran. razini66@yahoo.com N M. H. Moradi Department of Electrical Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran. mhmoradi@basu.ac.ir Y S. M. Hosseinian Department of Civil Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran. s.hosseinian@basu.ac.ir N 10.22068/IJEEE.13.1.5 Multi agent systems (MAS) are popularly used in practice, however; a few studies have looked at MAS capabilities from the power engineering perspective. This paper presents the results of an investigation concerning the compatibility of MAS capabilities in different power engineering categories. Five MAS capabilities and seven power system categories are established. A framework for applying MAS in power engineering is developed. A fuzzy inference system is adopted to evaluate the paper proposed framework. Two approaches, namely simulation and real, are considered for different power categories. The paper shows that MAS capabilities are generally compatible with both approaches, although compatibility of MAS with real approach is more significant. The paper concludes that in the near future MAS is anticipated to be a key important tool in the development of intelligent systems and smart grids in power system. This paper contributes to thinking on perspective of MAS in power System. Multi Agent Systems,Power System,Fuzzy Assessment, http://ijeee.iust.ac.ir/article-1-992-en.html http://ijeee.iust.ac.ir/article-1-992-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 13 1 2017 3 1 Assessing Circuit Breaker’s Electrical Contact Condition through Dynamic Resistance Signature Using Fuzzy Classifier 57 67 EN M. Khoddam Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. marya.khoddam@stu.um.ac.ir N J. Sadeh Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. sadeh@um.ac.ir Y P. Pourmohamadiyan Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. pourmohamadiyan@um.ac.ir N 10.22068/IJEEE.13.1.6 Circuit Breakers (CBs) are critical components in power system for reliability and protection. To assure their accurate performance, a comprehensive condition assessment is of an imminent importance. Based on dynamic resistance measurement (DRM), this paper discusses a simple yet effective fuzzy approach for evaluating CB’s electrical contacts condition. According to 300 test results obtained from healthy and three defected electrical contacts, the authors describe the special effect of common failures on DRM characteristics and propose seven deterioration indicators. Using these parameters, a fuzzy classifier is suggested to accurately determine contact sets condition. The salient advantage of the proposed model is its capability to recognize the type of contact failure. The feasibility and effectiveness of the proposed scheme has been validated through 40 real life recorded data of some electrical contacts.  Condition Assessment,Circuit Breaker (CB),Electrical Contact,Dynamic Resistance Measurement (DRM),Fuzzy Logic,Fuzzy Classifier, http://ijeee.iust.ac.ir/article-1-1030-en.html http://ijeee.iust.ac.ir/article-1-1030-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 13 1 2017 3 1 Analysis and Comparison Study of Novel Stator-Segmented Switched Reluctance Motor 68 76 EN S. R. Mousavi-Aghdam Department of Electrical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran. r.mousaviaghdam@uma.ac.ir Y M. R. Feyzi Faculty of Electrical & Computer Engineering, University of Tabriz, Tabriz, Iran. feyzi@tabrizu.ac.ir N N. Bianchi Faculty of Industrial Engineering, University of Padova, Padova, Italy. nicola.bianchi@unipd.it N 10.22068/IJEEE.13.1.7 This paper presents analysis and comparative study of a novel high-torque three-phase switched reluctance motor (SRM) with magnetically isolated stator segments. In the proposed SRM, each segment has a concentric winding located on the center body of it and two diametrically opposite windings which form the motor phase. There are four salient poles in the stator segment. Two of them share their flux path in the center body of the segment. The rotor has a solid structure including twenty two salient poles. In this unique SRM, stator segments topology, number of the stator segments poles and the rotor poles, and angular distance of the stator segments are selected so that the motor properly operates in both directions. Two-phase design with different pole combination is also possible. During operation, there are short flux paths along two adjacent rotor poles and excited segment poles. Therefore, the proposed SRM has all benefits of the short flux path structures. The principle and fundamentals of the proposed SRM design are detailed in the paper. The motor is analysed using finite element method (FEM) and some comparisons are reasonably carried out with other SRM configurations. Finally, a prototype motor is built and experimental results validate the performance predictions in the proposed motor. Reluctance Machines,Switched Reluctance Motor (SRM),Segmented Stator,High-Torque Design,Finite Element Method (FEM), http://ijeee.iust.ac.ir/article-1-1000-en.html http://ijeee.iust.ac.ir/article-1-1000-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 13 1 2017 3 1 Stator Turn-to-Turn Fault Detection of Induction Motor by Non-Invasive Method Using Generalized Regression Neural Network 77 88 EN H. Yaghobi Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran. yaghobi@semnan.ac.ir Y 10.22068/IJEEE.13.1.8 Condition monitoring and protection methods based on the analysis of the machine's current are widely used according to non-invasive characteristics of current transformers. It should be noted that, these sensors are installed by default in the machine control center. On the other hand, condition monitoring based on mathematical methods has been proposed in literature. However, they are model based and are too complex. Artificial neural network (ANN) methods are robust and less model dependent for fault diagnosis when the fault signature can be directly achieved using the sampling data. In this procedure, the state of internal process will be ignored. Therefore, generalized regression neural network (GRNN) based method is presented in this paper that uses negative sequence currents (calculated from the machine's currents) as inputs to detect and locate an inter-turn fault in the stator windings of the induction motor. Turn-to-turn fault by changing the contact resistance and various numbers of shorted turns for realizing the fault severity has been modeled by Matlab/Simulink. The simulation and experimental results show that the proposed method is effective for the diagnosis of stator inter-turn fault in induction motor under the supply voltage unbalances. Diagnosis,Induction Machine,Turn-to-Turn Fault,GRNN,Non-Invasive Method,Negative Sequence Current, http://ijeee.iust.ac.ir/article-1-974-en.html http://ijeee.iust.ac.ir/article-1-974-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 13 1 2017 3 1 A Novel Interleaved DC-DC Converter with Reduced Loss for Fuel Cell Vehicle Application 89 99 EN M. Khalilzadeh School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. m.khalilzadeh@ut.ac.ir N B. Asaei School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. basaei@ut.ac.ir Y M. R. Nikzad School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. nikzad@ut.ac.ir N 10.22068/IJEEE.13.1.9 In this paper a novel four-leg interleaved DC-DC boost converter is proposed which is well suitable for fuel cell vehicles (FCV) application. The voltage stress of two switches of this converter is half of the conventional interleaved converters. Therefore, smaller and cheaper switches can be utilized. Also "on" state duration of the two of four switches are reduced in comparison with conventional converter. Furthermore, comparing the losses of the proposed converter to conventional one – which is used in ،Toyota Mirai 2015 – shows a significant loss reduction in full power range. The proposed converter is simulated within an FCV in urban and highway driving cycles using ADVISOR software. The results show that the average power loss of the converter is improved about 32% in urban cycle and about 17% in highway cycle comparing to conventional one. Fuel Cell Vehicle,Interleaved DC-DC Converter,Converter Loss Calculation,Loss Reduction,Toyota Mirai 2015, http://ijeee.iust.ac.ir/article-1-1018-en.html http://ijeee.iust.ac.ir/article-1-1018-en.pdf

Iran University of Science and Technology (IUST) Iranian Journal of Electrical and Electronic Engineering 1735-2827 1735-2827 13 1 2017 3 1 Training Radial Basis Function Neural Network using Stochastic Fractal Search Algorithm to Classify Sonar Dataset 100 111 EN M. R. Mosavi Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran. M_Mosavi@iust.ac.ir Y M. Khishe Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran. m_khishe@elec.iust.ac.ir N Y. Hatam Khani Department of Electrical Engineering, University of Imam Khomeini Marine Sciences, Noshahr, Iran. yousofhatamkhani@yahoo.com N M. Shabani Department of Physics, University of Hormozgan, Bandar Abbas, Iran. shabani.stu@Hormozgan.ac.ir N 10.22068/IJEEE.13.1.10 Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorithms have been conventional to training RBF network in the recent years. This study uses Stochastic Fractal Search Algorithm (SFSA) for training RBF NNs. The particles in the new algorithm explore the search space more efficiently by using the diffusion property, which is observed regularly in arbitrary fractals. To assess the performance of the proposed classifier, this network will be evaluated with the two benchmark datasets and a high-dimensional practical dataset (i.e., sonar). Results indicate that new classifier classifies sonar dataset six percent better than the best algorithm and its convergence speed is better than the other algorithms. Also has better performance than classic benchmark algorithms about all datasets. Classifier,RBF,Stochastic Fractal,Meta-Heuristic Algorithm, http://ijeee.iust.ac.ir/article-1-959-en.html http://ijeee.iust.ac.ir/article-1-959-en.pdf