Enhanced Grey Wolf Optimization with a Novel Pouncing Weight Strategy for MPPT in PV Systems Under Partial Shading

Grey Wolf Optimization (GWO) is a bio-inspired metaheuristic algorithm used in photovoltaic systems for maximum power point tracking (MPPT). This algorithm is known for its robustness and accuracy, but can still be improved. Enhanced Gray Wolf Optimization (EGWO) is a novel variant of GWO that introduces a new hunting strategy, when the wolves are close to the prey, they can now pounce toward it. This pouncing strategy drastically reduces the tracking time and reduce oscillations. In this paper, we will introduce a new parameter to this algorithm, which is an adaptive pouncing weight (PW_EGWO). The main strategy behind it is to allow the wolves to have a dynamic pouncing distance to optimize the exploration and exploitation around the global maximum power point (GMPP), ensuring precise convergence with minimal tracking time. The effectiveness of the proposed strategy is validated by numerical results obtained via MATLAB Simulink simulations.