The global demand for renewable energy is growing, and one of the proposed solutions to this energy crisis is the use of photovoltaic systems. So far, they are a reliable solution, as they are nonpolluting and can be used almost anywhere on the planet. However, the design and development of more efficient photovoltaic cells and modules require an accurate extraction of their intrinsic parameters. Up to date, metaheuristic algorithms have proven to be the best methods to obtain accurate values of these intrinsic parameters. Hence, to extract these parameters reliably and accurately, this paper presents an optimization method based on the principle of bald eagle search (BES) during fish hunting. This search is divided into three steps: in the first stage (space selection), the eagle selects the space with the largest number of prey; in the second stage (space search), the eagle moves into the selected space to search for prey; in the third stage (dive), the eagle swings from the best position identified in the second stage and determines the best point to hunt. Thus, we used the proposed BES algorithm to determine the parameters of the single-diode model (SDM), the double-diode model (DDM), and the PV modules. This algorithm converges very quickly and gives a root mean square error (RMSE) of for the single-diode model and for the dual-diode model. The results obtained show that the proposed algorithm is more efficient than the other methods available in the literature, in terms of the better accuracy of the results obtained. The good harmony of the I-V and P-V characteristic curve of the calculated parameters with that of the measured data from a PV module/cell data sheet proves that the proposed BES should be used among the methods provided in the literature for the identification of PV solar cell parameters.

中文翻译：

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使用白头鹰搜索 (BES) 算法的光伏系统参数估计

全球对可再生能源的需求正在增长，而针对这一能源危机提出的解决方案之一是使用光伏系统。到目前为止，它们是一种可靠的解决方案，因为它们是无污染的，几乎可以在地球上的任何地方使用。然而，更高效的光伏电池和模块的设计和开发需要准确提取其固有参数。迄今为止，元启发式算法已被证明是获得这些内在参数的准确值的最佳方法。因此，为了可靠、准确地提取这些参数，本文提出了一种基于白头鹰搜索（BES）原理的鱼类狩猎优化方法。这个搜索分为三个步骤：第一阶段（空间选择），老鹰选择猎物数量最多的空间；第二阶段（空间搜索），老鹰进入选定空间寻找猎物；在第三阶段（下潜），老鹰从第二阶段确定的最佳位置摆动，并确定最佳捕猎点。因此，我们使用所提出的 BES 算法来确定单二极管模型 (SDM)、双二极管模型 (DDM) 和 PV 模块的参数。该算法收敛速度非常快，并给出的均方根误差 (RMSE) 为对于单二极管模型和双二极管模型。获得的结果表明，所提出的算法比文献中可用的其他方法更有效，就获得的结果的准确性而言更好。计算参数的 IV 和 PV 特性曲线与来自 PV 模块/电池数据表的测量数据的良好协调证明，建议的 BES 应在文献中提供的用于识别 PV 太阳能电池的方法中使用参数。