Fault Diagnosis for PV System Using a Using 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.

Comparative analysis of the performance of supervised learning algorithms for photovoltaic system fault diagnosis

“…In order to give crucial information for the prediction of faults, prediction models often depend on evaluating statistical data generation over time and long-term meteorological data [24]. In research [5], neural network techniques were used to forecast the produced energy by PV systems. The temperature of the PV modules has also been predicted in [25].…”

“…Some of the techniques used for machine learning approaches are listed below: ANN: Networks of Multilayer Perceptron type are used in most of the research. ANN-based methods for predicting faults in PV systems are given a lot of attention [5]. SVM: They are used to diagnose faults in PV systems using a time series analytic method, and a lot of interest is given to these techniques [22].…”

“…Adaptive Neuro-Fuzzy Inference Systems (ANFIS) have been used for fault diagnosis as well [26]. Another hybrid model that combines both Back Propagation Neural Network and Particle Swarm Optimization technique (BPNN-PSO) has also been used for fault diagnosis in PV systems [5].…”

“…The characteristic that gives the best results in the classification of the training data will result in the best split. The formulas of entropy and information gain that are used for classification are described in equations ( 4) and (5).…”

“…PV electrical data are used for fault diagnosis such as output power, output voltage, current at the DC or AC side, and the current-voltage characteristic (I-V curve) [5]. The fault diagnosis using the I-V curve is an important method [6] because the I-V curve gives rich information about the health state of PV modules.…”