Abstract Harvesting maximum power from a partially shaded photovoltaic array is a critical issue that attracts the attention of several researchers. As per the literature, it is found that providing an optimal reconfigured pattern of the shaded photovoltaic array is an optimal solution for this issue. Therefore, in this paper, an innovative fitness function has been considered with the artificial ecosystem-based optimization for an electrical photovoltaic array reconfiguration approach. The proposed approach has been applied for the large scale photovoltaic arrays including 9 × 9 , 6 × 20 , 16 × 16, and 25 × 25 photovoltaic array with different shade patterns. The new fitness function has been validated via a comparison with the regular used weighted function in literature. The quality of the solutions of the proposed artificial ecosystem-based optimization–reconfiguration approach has been assessed and demonstrated via performing several measures namely fill factor, percentage of power loss, mismatch power loss, and power enhancement in comparison with a total cross-tied, particle swarm optimizer approaches, and harris hawks optimizer. Furthermore, the Wilcoxon signed-rank test has been performed to illustrate the applicability, robustness, and consistency of the proposed algorithm results across several independent runs. The analysis reveals the quality of the innovative fitness function while integrating with the optimization algorithms in comparison to the weighted fitness function in producing higher power values via attaining a more efficient photovoltaic array design. Furthermore, the results confirmed the efficiency of the artificial ecosystem-based optimization–photovoltaic reconfiguration approach in boosting the generated photovoltaic power by a percentage of 28.688%, 7.0197 %, 29.2565%, 8.3811% and 5.3884 % across the considered systems with an uniform dispersion of the shadow on the photovoltaic surface and providing highest consistent in the maximum power values across the independent runs.

中文翻译：

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一种基于人工生态系统优化的新型目标函数，用于缓解大型光伏阵列的失配功率损失

摘要 从部分遮蔽的光伏阵列中获取最大功率是一个关键问题，引起了一些研究人员的注意。根据文献，发现提供遮蔽光伏阵列的最佳重新配置图案是该问题的最佳解决方案。因此，在本文中，已经考虑了一种创新的适应度函数和基于人工生态系统的优化，用于电气光伏阵列重新配置方法。所提出的方法已应用于大型光伏阵列，包括具有不同阴影图案的 9×9、6×20、16×16 和 25×25 光伏阵列。新的适应度函数已通过与文献中常规使用的加权函数的比较得到验证。所提出的基于人工生态系统的优化-重构方法的解决方案的质量已通过执行多项措施进行评估和证明，即填充因子、功率损耗百分比、失配功率损耗和与总交叉连接相比的功率增强，粒子群优化器方法和 harris hawks 优化器。此外，已执行 Wilcoxon 符号秩检验以说明所提出算法结果在多次独立运行中的适用性、稳健性和一致性。与加权适应度函数相比，该分析揭示了创新适应度函数的质量，同时与优化算法相比较，通过获得更有效的光伏阵列设计来产生更高的功率值。此外，