Abstract The high efficiency triple-junction solar cells (TJSC) have received considerable attention in the concentrated PV systems nonetheless the harvested electrical energy generated by TJSC-based system has been reduced under the partial shading conditions. Tracking the global maximum power point in the TJSC-based system characteristics is a main challenge faced the traditional trackers like perturb and observe (P&O). Therefore, this paper proposes a new global maximum power point tracker (MPPT) based on recent metaheuristic approach of Manta ray foraging optimization (MRFO). The proposed MRFO based MPPT is employed to extract the global maximum power point (GMPP) from the Triple-Junction solar based array operated under shadow conditions. Seven shadow patterns are studied on seven topologies of triple junction solar based arrays. The obtained results are compared with differential evolution (DE) and crow search algorithm (CSA). The obtained results confirmed the superiority of the proposed MPPT based MRFO in extracting the GMPP under different partial shadow patterns followed by CSA and DE optimizers.

中文翻译：

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使用蝠鲼觅食优化算法减轻基于三结太阳能电池系统的部分阴影的稳健全局 MPPT

摘要 高效三结太阳能电池（TJSC）在聚光光伏系统中受到了广泛的关注，但在部分遮蔽条件下，基于TJSC的系统产生的收集电能有所减少。在基于 TJSC 的系统特性中跟踪全局最大功率点是传统跟踪器（如扰动观察 (P&O)）面临的主要挑战。因此，本文基于最近的蝠鲼觅食优化（MRFO）元启发式方法提出了一种新的全局最大功率点跟踪器（MPPT）。提出的基于 MRFO 的 MPPT 用于从在阴影条件下运行的三结太阳能阵列中提取全局最大功率点 (GMPP)。在三结太阳能阵列的七种拓扑结构上研究了七种阴影模式。所得结果与差分进化（DE）和乌鸦搜索算法（CSA）进行了比较。获得的结果证实了所提出的基于 MPPT 的 MRFO 在不同部分阴影模式下提取 GMPP 的优越性，然后是 CSA 和 DE 优化器。