Automatic fault detection and diagnosis for photovoltaic systems using combined artificial neural network and analytical based methods

Long term exposure of photovoltaic (PV) systems under relatively harsh and changing environmental conditions can result in fault conditions developing during the operational lifetime. The present solution is for system operators to manually perform condition monitoring of the PV system. However, it is time-consuming, inaccurate and dangerous. Thus, automatic fault detection and diagnosis is a critical task to ensure the reliability and safety in PV systems. The current state-of-the-art techniques either cannot provide enough detailed fault information with high accuracy or have too much complexity. This work presents an automatic fault detection and diagnosis method for string based PV systems. It combines an artificial neural network (ANN) with the conventional analytical method to conduct the fault detection and diagnosis tasks. A two-layered ANN is applied to predict the expected power which is then compared with the measured power from the real PV system. Based on the difference between the ANN estimated power and the measured power, the open circuit voltage and short circuit current of the PV string determined using analytical equations are used to identify any of the six defined fault types. The proposed method has a fast detection, compact structure and good accuracy. Simulation results show the effective fault detection and diagnosis capability of the proposed method.