Fault diagnosis of PV array by using RBF neural network optimized by particle swarm
WANG Fuzhong
PEI Yulong
Abstract：Normal or abnormal operation of PV array is closely linked to the security and reliability of PV system.BP neural network fault diagnosis algorithm in PV array have some problems such as low accuracy,slow convergence speed,and so on.In order to solve these problems,a method of fault diagnosis of PV array using RBF neural network optimized by particle swarm is put forward.The PV array fault diagnosis model is established,which uses PV array four characteristic parameters as input variables and five normal circumstances as output variables.The method of adaptive network weight optimization based on particle swarm algorithm is simulated.Finally,the algorithm proposed,the traditional BP neural network algorithm and traditional RBF neural network algorithm are compared.The simulation experiment shows that the proposed algorithm can not only effectively diagnose the fault types of PV array,but also improve accuracy of fault diagnosis.
Keywords：PV arraysfault diagnosisRBF neural networkparticle swarm algorithm
Publication Date：2018-03-02
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 93-98 )