Abstract Combining with cuckoo search (CS) algorithm, this paper proposes a novel dynamic particles maximum power point tracking (MPPT) method to track global maximum power point (GMPP) for photovoltaic (PV) systems under partial shading conditions (PSCs). Based on the investigation of the transient characteristic of the boost converter, the concept of dynamic sample time is introduced and deployed to decrease system tracking time with a fuzzy positioning mechanism. In the proposed two-mode searching strategy, numbers of particles, which are gradually abandoned through several iterations in global mode, rapidly position the area near the GMPP. Then the local mode is activated to accurately track the GMPP. These implements allow traversal search of numbers of initial particles in overall area to handle random shading patterns of PV arrays with fewer iterations and less sample time compared to conventional optimization algorithm based MPPT techniques. Thus, the success rate of tracking GMPP is maximized with shorter tracking time and lower tracking power loss under PSCs. Comprehensive simulations and experiments are performed to verify the better effectiveness of the proposed dynamic particles MPPT method in comparison to other existing advanced optimization algorithms based MPPT techniques in PV systems under PSCs.

中文翻译：

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局部遮光条件下光伏系统的动态粒子MPPT方法

摘要 结合布谷鸟搜索（CS）算法，提出了一种新的动态粒子最大功率点跟踪（MPPT）方法来跟踪部分遮蔽条件（PSC）下光伏（PV）系统的全局最大功率点（GMPP）。在对升压转换器瞬态特性的研究的基础上，引入并部署了动态采样时间的概念，以通过模糊定位机制减少系统跟踪时间。在提出的双模式搜索策略中，在全局模式下通过多次迭代逐渐放弃的粒子数量迅速定位 GMPP 附近的区域。然后激活本地模式以准确跟踪GMPP。与基于 MPPT 技术的传统优化算法相比，这些工具允许遍历搜索整个区域的初始粒子数量，以更少的迭代和更少的采样时间来处理 PV 阵列的随机阴影模式。因此，跟踪 GMPP 的成功率在 PSC 下以更短的跟踪时间和更低的跟踪功率损耗最大化。进行了全面的模拟和实验，以验证所提出的动态粒子 MPPT 方法与 PSC 下光伏系统中其他现有的基于 MPPT 技术的先进优化算法相比具有更好的有效性。