A Novel Fault Diagnosis Method Based-on Modified Neural Networks for Photovoltaic Systems

摘要

The main purpose of this paper is to propose an intelligent fault diagnostic method for photovoltaic (PV) systems. First, Solar Pro software package was used to simulate a photovoltaic system for gathering power generation data of photovoltaic modules during normal operations and malfunctions. Then, the collected power generation data was used to construct matter-element models based on extension theory for PV systems. The matter-element model combines with the neural networks to form an intelligent fault diagnosis system for PV systems. The proposed fault diagnosis method was adopted to identify the faulty types of a 3.15kW PV system. The simulation results indicate that the proposed fault diagnosis method can detect the malfunction types of PV system rapidly and accurately with less time and memory consumption.