A Numerical Evaluation Performance of Bio-inspired Metaheuristic MPPT Algorithms for Low-power PV Systems

Abstract

Photovoltaic (PV) systems require efficient maximum power point tracking (MPPT) to ensure optimal energy utilization. Conventional methods such as Perturb and Observe (P&O) and Incremental Conductance (InC) still exhibit oscillations, slow response, and a tendency to get trapped at local maxima under changing irradiation. This study evaluates five bio-inspired metaheuristic algorithms, consisting of Grey Wolf Optimizer (GWO), Sand Cat Swarm Optimization (SCSO), Horse Herd Optimization (HHO), Chameleon Swarm Algorithm (CSA), and Flying Squirrel Search Optimization (FSSO), to improve tracking accuracy, convergence speed, and MPPT stability. All algorithms were tested under identical parameter settings on a PV model with a DC–DC boost converter, under static and dynamic irradiance conditions, using MATLAB/Simulink. Results show that HHO achieved the best performance, with efficiencies of 99.96% at 1000 W/m² and 98.03% at 800 W/m², tracking time < 0.05 s, and power ripple < 0.3%. Therefore, HHO demonstrates superior performance and shows strong potential for PV system implementation.