EVALUATION OF METAHEURISTIC TRACKING METHODS IN PHOTOVOLTAIC WATER PUMPING UNDER PARTIAL SHADING
DOI:
https://doi.org/10.59277/RRST-EE.2026.3.2Keywords:
Photovoltaic (PV), Water pumping, Particle swarm optimization (PSO), Grey wolf optimization (GWO), Marine predator algorithm (MPA)Abstract
The use of photovoltaic (PV) energy for water pumping is one of the most promising applications of renewable energy systems. This paper proposes advanced maximum power point tracking (MPPT) techniques to improve the performance of PV-based water-pumping systems under partial shading conditions. The proposed system consists of a PV generator, a DC–DC converter, and a brushless DC (BLDC) motor-driven water pump. Conventional MPPT techniques, such as Perturb and Observe (P&O), fail to track the global maximum power point (GMPP) under partial shading conditions. Therefore, particle swarm optimization (PSO), grey wolf optimization (GWO), and marine predator algorithm (MPA)-based approaches are investigated to ensure effective global maximum power point tracking (GMPPT). The proposed methods are evaluated in MATLAB/Simulink across various partial-shading scenarios. The results demonstrate improved tracking performance compared with the conventional P&O method and provide a comparative assessment of the investigated metaheuristic techniques in terms of convergence behavior and steady-state performance.
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