A Novel Modified Shuffled Frog Leaping Algorithm (MSFLA) MPPT Controller for Photovoltaic Systems

The exhaustion of fossil fuel reserves and increasing environmental issues have intensified the need for renewable energy. The focus on enhancing solar energy efficiency is particularly strong among various renewable sources. In photovoltaic (PV) systems, temperature and solar irradiance are crucial factors that significantly affect performance, making it essential to operate these systems at their maximum power point. Specifically for PV systems operating in partial shade, this study introduces a new method for improving the performance of Maximum Power Point Tracking (MPPT) controllers. The proposed method integrates a Modified Shuffled Frog Leaping Algorithm (MSFLA) with the MPPT controller to maximize power extraction from PV panels. Inspired by the social dynamics of frog populations, the MSFLA has been tailored to address the complexities of partial shading, which often results in multiple local maxima on the power-voltage curve. Comparative analysis reveals that MSFLA achieves the highest tracking speed and output power accuracy among bio-inspired optimization algorithms, achieving an efficiency of 99.24%.