The fault diagnosis method of photovoltaic module based on probabilistic neural network

This paper describes a fault diagnosis method of photovoltaic (PV) module, which bases on equivalent circuit module and probabilistic neural network (PNN). The output characteristics of the PV module under normal, dust deposition, abnormal aging and partial shading conditions are simulated by using the equivalent circuit model. The simulated data are used as characteristic parameters to fault type diagnosis. The performance of the fault diagnosis model is evaluated, and the results indicate that the method can detect the fault types correctly.