A novel fault diagnosis method for photovoltaic array based on BP-Adaboost strong classifier

Abstract

Accurate fault diagnosis of photovoltaic (PV) array is important for effective operation of PV systems. The back propagation neural network (BPNN) based classifier model has wide application in fault diagnosis for PV array. Due to insufficient accuracy obtained by using single BPNN, this paper proposes a novel fault diagnosis scheme based on BP-Adaboost strong classifier. Firstly, several indicators constitute an effective feature vector which is applied to build several BPNN based weak classifier models. Secondly, Adaboost algorithm is adopted to build a strong classifier by combining those weak classifiers into the final output with certain weights. Four operation conditions including normal condition, short circuit fault, partial shade fault, open circuit fault can be accurately identified by the proposed method. Dataset from a 1.8 kW grid-connected PV system with 6 × 3 PV array are applied to experimentally test the performance of the developed method.