A New War Strategy Optimization Algorithm based Maximum Power Point Tracking Method for PV Systems under Partial Shading Conditions

Solar energy systems are well known for their eco-nature and cost-effectiveness as they gain more traction with lower installation expenses and enhanced efficiency levels over time. Traditional maximum power point tracking (MPPT) methods perform effectively in uniform irradiance conditions but encounter difficulties in identifying global maximum power points (GMPP) during partial shading conditions (PSCs). Although various advanced methods exist to tackle this challenge such as meta heuristic approaches it is evident that there is potential for further enhancement, in accelerating the convergence process towards the GMPP. This study presents an approach for maximizing power point tracking (MPPT) using War Strategy Optimization (WSO) which imitates the tactical movements of military forces in combat situations. The optimization procedure imitates battlefield tactics by having soldiers adapt their positions in time to reach an optimal outcome. Two primary war tactics—attack and defensive—are simulated within this model. In order to improve the effectiveness and resilience of the algorithm a novel weight adjustment mechanism and a strategy for relocating soldiers have been integrated. The effectiveness of the WSO algorithm was tested with more than 25 benchmark functions, demonstrating significant improvements in performance compared to well-known metaheuristic algorithms from the existing literature. Th proposed WSO algorithm seems to find a middle ground between exploring and exploiting PSC based photovoltaic systems. The simulation results show that it outperforms sophisticated MPPT techniques. In contrast to AI-based MPPT methods, the proposed WSO demonstrates faster tracking speed, improved dynamic response, higher static and dynamic tracking efficiency, better power tracking, and greater accuracy, even in complex scenarios involving multiple shaded areas.