Fault Diagnosis Method for Photovoltaic Arrays Based on Support Vector Machines

Abstract

A fault diagnosis method for photovoltaic arrays based on Support Vector Machines (SVM) is proposed to address four typical faults of photovoltaic arrays (short-circuit, open-circuit, aging, shadowing). MATLAB is used to simulate the faults in the photovoltaic array, obtaining four characteristic parameters under different faults: short-circuit current (Isc), open-circuit voltage (Uoc), current at the maximum power point (Im), and voltage at the maximum power point (Um). These parameters are used as training samples to establish a classification model through the SVM algorithm for training and verification. Simulation results show that this method can accurately diagnose the typical faults of the photovoltaic array, and the fault diagnosis accuracy is high.