On-line monitoring and fault diagnosis of PV array based on BP neural network optimized by genetic algorithm

Abstract

The vast majority of photo voltaic (PV) arrays often work in harsh outdoor environment, and undergo various fault, such as local material aging, shading, open circuit, short circuit and so on. The generation of these fault will reduce the power generation efficiency, and even lead to fire disaster which threaten the safety of social property. In this paper, an on-line distributed monitoring system based on ZigBee wireless sensors network is designed to monitor the output current, voltage and irradiate of each PV module, and the temperature and the irradiate of the environment. A simulation PV module model is established, based on which some common faults are simulated and fault training samples are obtained. Finally, a genetic algorithm optimized Back Propagation (BP) neural network fault diagnosis model is built and trained by the fault samples data. Experiment result shows that the system can detect the common faults of PV array with high accuracy.