A Comparison of GWO and PSO for MPPT in Solar Photovoltaic Stand alone System

Electricity production from solar energy gained a lot of recognition on a global scale as because of its copious availability and also environmentally beneficial quality. The availability of the electricity created from the sun may fluctuate depending on a number of circumstances, including shifts in irradiation, temperature, and shade, amongst others, Therefore, in recent, research has been focused on the Maximum Power Point Tracking (MPPT) approach with the purpose of extracting the most power possible from photovoltaic solar panels. The Hill-Climbing and Incremental Conductance MPPT techniques popular choices among the several ways that were developed for achieving Maximum Power while being exposed to continual irradiation. However, when exposed to changes in environmental circumstances, these approaches display poor dynamic performance, and substantial steady-state oscillations near MPP. bio-inspired algorithms demonstrated outstanding performance when confronted with non-linear, non-differentiable, and stochastic optimization problems, all while avoiding the need an excessive amount of mathematical calculations, in this paper utilizing the Grey Wolf Optimization technique (GWO) and the Particle Swarm Optimization technique (PSO), with a focus on starting value selection. The capacity to measure the global peak power precisely under changing environmental circumstances with practically minimal steady-state oscillations, quicker dynamic reaction and straightforward implementation are some of important aspects of this technology. A methodical examination was carried out under various settings, including varying degrees of solar irradiation, and lastly, the findings produced were compared between the two established methodologies. In addition, the accuracy of this suggested technique was validated by utilizing MATLAB/Simulink as the simulation software.