Accurate parameters’ identifications of photovoltaic models is essential for precise simulation and analysis of integrated and standalone photovoltaic systems which is directly influencing performance assessments. Accordingly, this study investigates the procedures of the hippopotamus optimizer for optimal parameters’ identifications of photovoltaic single and double-diode models, as well as the Sandia photovoltaic array performance model. The single and double-diode models simulate the steady-state I-V and P-V principal curves, while the Sandia model predicts maximum power points under various environmental conditions. Reducing root mean quadratic error is adapted as the optimization objective, subjected to operational and design viable constraints. The hippopotamus optimizer’s performance is tested on eight commercial photovoltaic units with diverse technologies, including silicon, poly-crystalline, mono-crystalline, cadmium telluride, copper indium gallium selenide, and amorphous silicon/microcrystalline silicon cells. Thru extensive simulations and comparisons with other optimizers in the literature, the hippopotamus optimizer shows its effectiveness in achieving lowest values of the root mean quadratic errors, indicating a high correlation among modeled and actual dataset points. For instance, using the single-diode model, the optimizer achieves best root mean quadratic error values of 28.210671 mA, 2.039979 mA, 13.79826 mA, 1.721864 mA, and 0.7728666 mA for Kyocera KC200GT, PhotoWatt PWP201, STP6-120/36, and STM6-40/36 modules and RTC France photovoltaic silicon cell, respectively. These results highlight the optimizer’s potential as a powerful tool for enhancing photovoltaic model accuracy. Consequently, the hippopotamus optimizer contributes to improved performance predictions and design precision in photovoltaic applications.

中文翻译：

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使用 hippopotamus optimizer 对不同技术的商业光伏电池/组件进行精确建模


光伏模型的准确参数识别对于集成和独立光伏系统的精确仿真和分析至关重要，这直接影响性能评估。因此，本研究研究了 hippopotamus 优化器用于光伏单二极管和双二极管模型以及 Sandia 光伏阵列性能模型的最佳参数识别的程序。单二极管和双二极管模型模拟稳态 I-V 和 P-V 主曲线，而 Sandia 模型预测各种环境条件下的最大功率点。减少均值根二次误差被调整为优化目标，但受到操作和设计可行的约束。河马优化器的性能在采用不同技术的 8 个商用光伏单元上进行了测试，包括硅、多晶、单晶、碲化镉、铜铟镓硒和非晶硅/微晶硅电池。通过与文献中其他优化器的广泛模拟和比较，hippopotamus 优化器显示了它在实现平均二次方根误差最低值方面的有效性，这表明建模和实际数据集点之间的高度相关性。例如，使用单二极管模型，优化器分别实现了 28.210671 mA、2.039979 mA、13.79826 mA、1.721864 mA 和 0.7728666 mA 的最佳均方根误差值，分别用于 Kyocera KC200GT、PhotoWatt PWP201、STP6-120/36 和 STM6-40/36 模块以及 RTC France 光伏硅电池。这些结果突出了优化器作为提高光伏模型准确性的强大工具的潜力。 因此，hippopotamus 优化器有助于提高光伏应用的性能预测和设计精度。