The field of research in maximum power point tracking (MPPT) methods is experiencing great progress with a wide range of techniques being suggested, ranging from simple but ineffective methods to more effective but complex ones. Therefore, it is very important to propose a strategy that is both simple and effective in controlling the global maximum power point (GMPP) for a photovoltaic (PV) system under changing weather conditions, especially in partial shading cases (PSCs). This paper proposes a new design of an MPPT controller based on a metaheuristic optimization technique called Crow Search Algorithm (CSA) to attenuate the undesirable effects of partial shading on the tracking performances of standalone PV systems. CSA is a nature-inspired method based on the intelligent skills of the crow in the search process of hidden food places. CSA technique combines efficiency and simplicity using only two tuning parameters. The stability analysis of the proposed system is performed using a Lyapunov function. The simulation results for three different partial shading cases that are zero, weak and severe shading confirm the superior performance of CSA compared to PSO and P&O techniques in term of easy implementation, high efficiency and low power loss, decreasing considerably the convergence time by an average of 38.53%.

中文翻译：

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使用乌鸦搜索算法 (CSA) 控制部分阴影光伏系统最大功率点的有效元启发式技术

最大功率点跟踪 (MPPT) 方法的研究领域正在经历巨大的进步，提出了广泛的技术，从简单但无效的方法到更有效但更复杂的方法。因此，提出一种既简单又有效的策略，在不断变化的天气条件下控制光伏 (PV) 系统的全局最大功率点 (GMPP) 非常重要，尤其是在部分阴影情况 (PSC) 中。本文提出了一种基于称为 Crow 搜索算法 (CSA) 的元启发式优化技术的 MPPT 控制器的新设计，以减弱部分阴影对独立光伏系统跟踪性能的不良影响。CSA 是一种基于自然启发的方法，基于乌鸦在隐藏食物地点搜索过程中的智能技能。CSA 技术仅使用两个调谐参数就结合了效率和简单性。所提出系统的稳定性分析是使用李雅普诺夫函数进行的。零、弱和重度阴影三种不同局部阴影情况的仿真结果证实了 CSA 与 PSO 和 P&O 技术相比在易于实现、高效率和低功耗方面的优越性能，平均显着缩短了收敛时间38.53%。