Abstract

The conventional energy sources, like fossil fuels, are considered unsuitable due to pollution; researchers are investigating new ways to source renewable energy, such as solar energy, which is transformed into electrical energy through photovoltaic cells, depending on prevailing weather conditions. The traditional maximum power point tracking techniques face challenges in identifying the maximum power point due to variable weather conditions, thereby diminishing the efficiency of the photovoltaic system. Optimizing the maximum power point tracking process is essential to address this issue, and any effective maximum power point tracking must adapt to changing environmental conditions. This paper presents a simulation and evaluation of a novel variable irradiance particle swarm optimization algorithm to improve the tracking rate and performance under fluctuating weather conditions (unlike the conventional particle swarm optimization methods). This method includes a current-sensing mechanism that detects 5% changes in current to reinitialize the parameters. The contributions of this work are declared in the development of an enhanced particle swarm optimization algorithm fitted for variable irradiance environments, Integration of a dynamic reset mechanism based on real-time current variations, and validation of performance through MATLAB/Simulink simulations using a single PV panel and comparing it with standard maximum power point tracking methods. The performance of the variable irradiance particle swarm optimization algorithm showed improvements in speed with a response time of less than 0.1s, reduced the steady state ripple by 1%, and efficiency for maximum power point tracking of 99%, with statistically validated performance using the Friedman test, confirming its robustness for practical photovoltaic applications.