Fault diagnosis for PV system using a deep learning optimized via PSO heuristic combination technique

Abstract

A heuristic particle swarm optimization combined with Back Propagation Neural Network (BPNN-PSO) technique is proposed in this paper to improve the convergence and the accuracy of prediction for fault diagnosis of Photovoltaic (PV) array system. This technique works by applying the ability of deep learning for classification and prediction combined with the particle swarm optimization ability to find the best solution in the search space. Some parameters are extracted from the output of the PV array to be used for identification purpose for the fault diagnosis of the system. The results using the back propagation neural network method only and the method of the back propagation heuristic combination technique are compared. The back propagation algorithm converges after 350 steps while the proposed BP-PSO algorithm converges only after 250 steps in the training phase. The accuracy of prediction using the BP algorithms is about 87.8% while the proposed BP-PSO algorithm achieved 95% of right predictions. It was clearly shown that the results of the back propagation heuristic combination technique had better results in the convergence of the simulation as well as in the accuracy of the prediction of the fault diagnosis in the PV system.