A Heuristic Diagnostic Method for a PV System: Triple-Layered Particle Swarm Optimization–Back-Propagation Neural Network

Abstract

This paper proposes a heuristic triple layered particle swarm optimization–back-propagation (PSO-BP) neural network method for improving the convergence and prediction accuracy of the fault diagnosis system of the photovoltaic (PV) array. The parameters, open-circuit voltage (Voc), short-circuit current (Isc), maximum power (Pm) and voltage at maximum power point (Vm) are extracted from the output curve of the PV array as identification parameters for the fault diagnosis system. This study compares performances of two methods, the back-propagation neural network method, which is widely used, and the heuristic method with MATLAB. In the training phase, the back-propagation method takes about 425 steps to convergence, while the heuristic method needs only 312 steps. In the fault diagnosis phase, the prediction accuracy of the heuristic method is 93.33%, while the back-propagation method scores 86.67%. It is concluded that the heuristic method can not only improve the convergence of the simulation but also significantly improve the prediction accuracy of the fault diagnosis system.

Keywords

• photovoltaic diagnosis system
• particle swarm optimization
• back-propagation neural network