An efficient metaheuristic optimization algorithm for optimal power extraction from PV systems under various weather and load-changing conditions

Abstract

Currently, the focus has been shifted towards exploring solar energy due to its environmentally friendly and economic nature. However, the efficiency of photovoltaic (PV) systems can be impacted by factors such as ineffective Global Maxima (GM) tracking, slow response time in tracking, becoming stuck in local maxima, and fluctuations around GM. To address these challenges, a new algorithm called horse herd optimization (HHO) has been applied to the maximum power point tracking (MPPT) controller. The proposed approach has four key features: high efficiency, cheap computing power, rapid MPPT, and zero oscillation. A comprehensive study compares the HHO technique with established methods such as perturb and observe (P&O), modified P&O (MP&O), incremental conductance (IC), Spline MPPT, particle swarm optimization (PSO), grasshopper optimization (GHO), and grey wolf optimization (GWO) across fast-changing irradiance, partial shading, complex partial shading, and load-changing conditions. All models and scenarios were implemented and tested in the MATLAB/Simulink environment. An adaptive search mechanism is integrated into HHO to improve its resilience. The results demonstrate that HHO shows robustness with the highest average tracking efficiency reaching 99.98 % with the least tracking time up to 160 msec while keeping the steady-state oscillation below 0.5 W. According to quantitative, comparative, and statistical results, the HHO-based MPPT performs better by achieving at least 21 % faster tracking time and 16 % faster settling time, and up to 4.4 % increase in power efficiency, which shows the effectiveness of the proposed technique.