Alshareef Muhannad J
Department of Electrical Engineering, College of Engineering and Computing in Al-Qunfudhah, Umm al-Qura University, Mecca, 24382, Saudi Arabia.
Sci Rep. 2025 May 30;15(1):19098. doi: 10.1038/s41598-025-04733-7.
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 metaheuristic 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 enhanced 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. To improve the effectiveness and resilience of the algorithm. Furthermore, this paper introduces an enhanced WSO algorithm designed for the MPPT task under partial shading conditions (PSC), incorporating several novel features such as adaptive rank-based weight updating, strategic soldier relocation, and a dual-mode attack-defense strategies. 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 and experimental results show that it outperforms advanced 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.
太阳能系统因其生态特性和成本效益而闻名，随着时间的推移，它们凭借更低的安装成本和更高的效率获得了越来越多的关注。传统的最大功率点跟踪（MPPT）方法在均匀光照条件下能有效运行，但在部分阴影条件（PSC）下识别全局最大功率点（GMPP）时会遇到困难。尽管存在各种先进方法来应对这一挑战，如元启发式方法，但显然在加速向GMPP的收敛过程方面仍有进一步提升的潜力。本研究提出了一种使用增强型战争策略优化（WSO）来实现最大功率点跟踪（MPPT）的方法，该方法模仿了军事力量在战斗情况下的战术行动。优化过程通过让士兵及时调整位置以达到最佳结果来模仿战场战术。在该模型中模拟了两种主要的战争战术——攻击和防御。为了提高算法的有效性和弹性。此外，本文介绍了一种为部分阴影条件（PSC）下的MPPT任务设计的增强型WSO算法，它包含了一些新颖的特性，如基于自适应秩的权重更新、战略性士兵重新定位和双模式攻击 - 防御策略。WSO算法的有效性通过25多个基准函数进行了测试，与现有文献中著名的元启发式算法相比，性能有显著提升。所提出的WSO算法似乎在探索和利用基于PSC的光伏系统之间找到了一个平衡点。仿真和实验结果表明，它优于先进的MPPT技术。与基于人工智能的MPPT方法相比，所提出的WSO展示了更快的跟踪速度、更好的动态响应、更高的静态和动态跟踪效率、更好的功率跟踪以及更高的精度，即使在涉及多个阴影区域的复杂场景中也是如此。
