Design of Modified Luo Converter with Enhanced MPPT Algorithm for PV Systems

Resumen

Photovoltaic (PV) systems are highly sensitive to partial shading conditions, which significantly reduce power extraction efficiency and introduce multiple local maxima in the power–voltage characteristics. This paper proposes a high-gain switched-inductor Modified Luo converter integrated with a Black Widow Optimized Artificial Neural Network (BWO-ANN) based Maximum Power Point Tracking (MPPT) technique for operation under near-zero partial shading conditions. The modified Luo converter enhances voltage gain and reduces current ripple through a switched-inductor configuration, operating in continuous conduction mode (CCM). The voltage conversion ratio is analytically derived using volt-second balance principles. The BWO algorithm optimally tunes ANN parameters to improve tracking speed and convergence accuracy under dynamic irradiance conditions. A high-frequency inverter and isolation transformer provide galvanic isolation between the PV source and DC bus/battery. MATLAB/Simulink validation demonstrates a converter efficiency of 97.79% and MPPT tracking efficiency of 98.96%, outperforming conventional P&O, PSO, and Hill-Climbing techniques. The proposed topology exhibits improved voltage gain, reduced ripple, fast convergence, and stable operation under varying environmental conditions.