MPPT Optimization with Improved Wolf Position Controller Parameters via Grey Wolf Algorithm Under Partial Shading Conditions

The Grey Wolf Optimization (GWO) Algorithm is an optimization method adopted from behavior of grey wolf animals. The Grey Wolf Optimization (GWO) Algorithm is a prominent metaheuristic method employed in Maximum Power Point Tracking (MPPT). The GWO Algorithm comprises three stages to achieve Global Maximum Power Point (GMPP) targets: Tracking, Encircling, and Attacking. The Improved Wolf Position Controller Parameters result from the development and modification of the $\mathbf{C}$ parameter equation in GWO, which is instrumental in regulating the positional distance between wolves and prey. These Improved Wolf Position Controller Parameters facilitate a balance between exploration and exploitation. This equilibrium is crucial in aiding algorithms to attain optimal solutions. Simulation results from the MPPT test indicate that the Improved GWO Algorithm demonstrates superior accuracy compared to the original GWO Algorithm and the Salp Swarm Algorithm. Specifically, the Improved GWO Algorithm achieves an average tracking accuracy of 97.4 %, whereas the GWO Algorithm and the Salp Swarm Algorithm attain accuracies of 91.28 % and 86.3 %.