Dynamic Monitoring and Optimization of Fault Diagnosis of Photo Voltaic Solar Power System Using ANN and Memetic Algorithm

Abstract

Most of the photo voltaic (PV) arrays often work in harsh outdoor environment, and undergo various faults, such as local material aging, shading, open circuit, short circuit and so on. The generation of these faults will reduce the power generation efficiency, and when a fault occurs in a PV model, the PV model and the systems connected to it are also damaged. In this paper, an on-line distributed monitoring system based on XBee wireless sensors network is designed to monitor the output current, voltage and irradiation of each PV module, and the temperature and the irradiation 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 memetic algorithm optimized Back Propagation ANN 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.