A Novel Photovoltaic Maximum Power Point Tracking Control Method Under Partial Shading Conditions

To enhance the power generation efficiency of photovoltaic (PV) systems, it is essential to ensure stable operation at the global maximum power point (MPP). This paper investigates the maximum power point tracking (MPPT) technique for PV systems and proposes an advanced MPPT technique based on the multi-subgroup parallel optimization algorithm with shuffled frog leaping algorithm (MSPO-SFLA), which integrates the improved particle swarm optimization algorithm with the shuffled frog leaping algorithm. The proposed MPPT technique not only enhances the accuracy and stability of MPP tracking but also effectively addresses the issue of traditional frog leaping algorithms falling into local optima under partial shading conditions (PSC). To validate the efficacy of the proposed method, a simulation model was developed in MATLAB. The simulation results demonstrate that this method significantly improves the operational efficiency of PV power generation systems, contributing to the advancement of the renewable energy sector and the progress of clean energy technology. Compared to traditional algorithms, this method exhibits clear advantages in achieving MPPT for PV power generation systems.