The operation of photovoltaic (PV) array under shade faces great challenges due to the power loss that reduces the extracted power. Reconfiguration process is one of the most promising solutions to reduce the effect of shadow on the array. However, the physical array relocation methods like total cross-tied (TCT) and Su Do Ku have some limitations due to their complicated arrangements. Therefore, this paper presents a recent metaheuristic approach of coyote optimization algorithm (COA) to solve the reconfiguration process of the partially shaded PV array. The main target is maximizing the global maximum power (GMP) extracted from the array. The proposed COA is applied on 99 PV array operated under four standard shadow patterns which are short wide (SW), long wide (LW), short narrow (SN), and long narrow (LN). The obtained configurations via the proposed COA method are compared to TCT, Su Do Ku, flower pollination algorithm (FPA), marine predators algorithm (MPA), and butterfly optimization algorithm (BOA) based arrangements. The best enhancement of GMP obtained via the proposed COA with respect to TCT configuration occurs in SW shadow pattern of 26.58% while the least on is 7.68% placed in SN pattern. The obtained results confirmed the competence and superiority of the proposed COA in reconfiguring the shaded array optimally.

中文翻译：

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基于郊狼优化算法的鲁棒光伏阵列重构策略，增强部分阴影条件下的提取功率

由于功率损耗降低了提取的功率，光伏（PV）阵列在阴影下的运行面临着巨大的挑战。重新配置过程是减少阴影对阵列影响的最有前途的解决方案之一。然而，全交叉连接（TCT）和Su Do Ku等物理阵列重定位方法由于其复杂的排列而具有一定的局限性。因此，本文提出了一种最新的郊狼优化算法（COA）元启发式方法来解决部分阴影光伏阵列的重新配置过程。主要目标是最大化从阵列中提取的全局最大功率（GMP）。所提出的 COA 应用于在四种标准阴影模式下运行的 99 个光伏阵列，即短宽 (SW)、长宽 (LW)、短窄 (SN) 和长窄 (LN)。通过所提出的 COA 方法获得的配置与基于 TCT、Su Do Ku、花授粉算法 (FPA)、海洋捕食者算法 (MPA) 和蝴蝶优化算法 (BOA) 的布置进行比较。通过提议的 COA 相对于 TCT 配置获得的 GMP 最佳增强出现在 SW 阴影模式中，达到 26.58%，而最少增强是 SN 模式中的 7.68%。获得的结果证实了所提出的 COA 在最佳地重新配置阴影阵列方面的能力和优越性。