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WANG Fuzhong,PEI Yulong.Photovoltaic Array Fault Identification Algorithm Based on Improved RBF Neural Network[J].JOURNAL OF POWER SUPPLY,2019,17(1):73-79
Photovoltaic Array Fault Identification Algorithm Based on Improved RBF Neural Network
Received:May 13, 2017  Revised:July 21, 2018
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DOI:10.13234/j.issn.2095-2805.2019.1.73
Keywords:photovoltaic(PV) array  fault diagnosis  RBF neural network  particle swarm optimization(PSO) algori-thm  genetic algorithm
Fund Project:Project Supported by The National Natural Science Foundation of China(61405055)、The Fundation of Industry University and Institute of Henan Province(132107000027)
     
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WANG Fuzhong School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo , China
PEI Yulong School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo , China 1585825925@qq.com
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Abstract:
      Photovoltaic(PV) array is an important part of the PV system. The traditional BP neural network diagno-sis algorithm has some disadvantages, such as low accuracy and slow convergence speed. To diagnose the location and types of fault in the PV array accurately, a fault diagnosis and identification algorithm based on the improved RBF neural network is put forward through analyzing five types of fault, i.e., open circuit, short circuit, aging, shadow, and panel fragmentation. Firstly, a PV array fault diagnosis model based on radial basis function(RBF) neural network is esta-blished. The method of determining the center of hidden layer of the fault model is formulated based on genetic algorithm, and then simulation experiments are conducted using the adaptive network weight optimization method based on particle swarm optimization(PSO) algorithm. Finally, the optimized algorithm and the traditional RBF neural network algorithm are com-pared. Results show that the proposed algorithm can not only diagnose the fault types of PV array effectively, but also improve the accuracy of fault diagnosis.
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