An Enhanced Artificial Gorilla Troops Optimizer-Based MPPT for Photovoltaic Systems

The low efficiency of photovoltaic (PV) systems arises from their nonlinear current-voltage characteristics, necessitating the use of maximum power point tracking (MPPT) techniques. Conventional MPPT methods are popular for their simplicity and low cost but exhibit poor performance under rapidly changing atmospheric conditions, leading to considerable energy losses. Under uniform solar irradiation, these traditional approaches can locate the maximum power Point (MPP), yet their reliance on small, fixed step sizes causes oscillations and output ripple. In dynamic environmental conditions, they often fail to accurately track the true MPP. To address these challenges, this paper proposes an MPPT strategy based on the artificial Gorilla Troops Optimizer (GTO) to enhance PV performance under partial shading conditions (PSCs) and fast climatic variations. An enhanced version of the algorithm (EnGTO) was developed to further improve MPPT efficiency. Comparative simulations with the perturb and observe (P&O) method and the classic GTO demonstrate that the proposed approach achieves rapid response to environmental changes and higher accuracy and lower oscillations under PSCs, reaching efficiencies of up to 99.96% (STCs) and 99.81% (PSCs).