An intelligent method for fault diagnosis in photovoltaic systems

This paper presents a diagnostic method based on I-V characteristic analysis and Artificial Neural Networks (ANN). A number of attributes are estimated using a simulation model based on a set of working conditions such as solar irradiance and module's temperature. The estimated attributes are then compared with those obtained from the real PV array measurements, which lead to the detection of possible faulty operating conditions. After detecting the presence of possible fault, tow algorithms are developed in order to isolate and identify eight different types of faults. The simulation results show that the proposed method can detect and classify the different faults occurring in a PV array with a high accuracy.