An Enhanced Particle Swarm Optimization Algorithm Fitting for Photovoltaic Max Power Tracking under Different Climatic Conditions

Under uniform irradiance, the Photovoltaic power-against-voltage curve has a nonlinear characteristic with a unique maximum power point that changes its position on the curve when subjected to a sudden change in solar irradiance. When the Photovoltaic string operates under partial shading conditions, the curve has several power peaks with only one Global Maximum Power Peak. The conventional maximum power point tracking strategies fail to deal with these attitudes. Many soft computing techniques are designed to deal with this issue, but the main challenges are to achieve that tracking with the quickest time, the minimum fluctuations, and the highest efficiency. In this paper, a modified Particle Swarm Optimization algorithm was proposed. It can exclude certain portions of the solution search area and redirects the newly created explorer particles into the promoted area until reach the Global max power point. The proposed method has been simulated for various power-against-voltage curves by using MATLAB / SIMULINK software. The results indicate that the proposed method outperforms the classical one with regards to the speed of Global Maximum Power Tracking with the lowest fluctuations and highest efficiency.