A Neural Network-Based Rapid Maximum Power Point Tracking Method for Photovoltaic Systems in Partial Shading Conditions

Abstract

The maximum power point tracking (MPPT) controller holds an important role in increasing the efficiency of the photovoltaic (PV) system. However, conventional MPPT techniques may fail to locate the global maximum power point (GMPP) under partial shading conditions (PSC). Hence, to optimize the efficiency of PV systems, we introduce a new MPPT technique for PSC. The proposed method employs an artificial neural network (ANN) to predict the area of the GMPP, and the classic perturb and observe (P&O) algorithm to locate the exact position of the GMPP. We validated the effectiveness of the technique using computer simulations performed with the MATLAB/Simulink program, the results of which verified that it can track the GMPP faster than other methods.