Khalifa Hadeer, Ebeed Mohamed, Magdy Gaber, Khaleel Sherif A, Shehata Mohamed I, Salah Moataz M, Jurado Francisco, Ali Hossam Hassan
Department of Electronics & Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Aswan, 81511, Egypt.
Department of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag, 82524, Egypt.
Sci Rep. 2025 Jun 2;15(1):19316. doi: 10.1038/s41598-025-98270-y.
The dependence on photovoltaic (PV) solar systems has increased dramatically to cover the increasing progress of world energy demand. Therefore, accurately specifying the parameters of PV modules is essential for evaluating the behavior and impact of integrating PV systems into electrical systems. In this context, a modified electric eel foraging optimization (MEEFO) is suggested for determining the parameters of solar PV modules. The proposed technique incorporates three improvement strategies: the fitness distance balance (FDB) strategy, fractional-order calculus (FOC), and quasiopposition-based learning (QOBL). These strategies enhance both exploitation and exploration capabilities while helping to prevent local optimization and premature convergence commonly observed in traditional EEFO. First, the proposed MEEFO is evaluated via two benchmark functions, including the basic and CEC 2019 benchmark functions. The results are then compared with those of other novel methods in terms of accuracy, convergence characteristics, and overall performance. The suggested MMEFO is then employed to identify the parameters for the single, double, and triple diode models of various PV cells/modules, including R.T.C. France, PVM752, STM6-40/36, PWP-201, and STP6-120/36. In addition, various meteorological data, such as changes in radiation and temperature, exist. The simulation findings demonstrate that MEEFO outperforms other techniques and is a reliable and superior method for accurately estimating PV module parameters. The application of MEEFO yields the lowest root mean square error (RMSE) values for the considered single, double, and triple diode models of R.T.C. France. Similarly, for STP6-120/36, the RMSE values are 1.660060E-02, 1.66006E-02, and 1.66089E-02, respectively. Additionally, for PWP-20, the RMSE values are 2.425075E-03, 2.42511E-03, and 2.42510E-03, respectively.
对光伏（PV）太阳能系统的依赖急剧增加，以满足世界能源需求日益增长的步伐。因此，准确确定光伏组件的参数对于评估将光伏系统集成到电气系统中的行为和影响至关重要。在此背景下，提出了一种改进的电鳗觅食优化算法（MEEFO）来确定太阳能光伏组件的参数。所提出的技术包含三种改进策略：适应度距离平衡（FDB）策略、分数阶微积分（FOC）和基于拟反对学习（QOBL）。这些策略增强了开发和探索能力，同时有助于防止传统电鳗觅食优化算法（EEFO）中常见的局部优化和早熟收敛。首先，通过两个基准函数对所提出的MEEFO进行评估，包括基本基准函数和CEC 2019基准函数。然后将结果在准确性、收敛特性和整体性能方面与其他新方法的结果进行比较。然后，所提出的MMEFO被用于确定各种光伏电池/组件（包括法国R.T.C.、PVM752、STM6 - 40/36、PWP - 201和STP6 - 120/36）的单二极管、双二极管和三二极管模型的参数。此外，还存在各种气象数据，如辐射和温度的变化。仿真结果表明，MEEFO优于其他技术，是一种准确估计光伏组件参数的可靠且优越的方法。对于法国R.T.C.所考虑的单二极管、双二极管和三二极管模型，MEEFO的应用产生了最低的均方根误差（RMSE）值。同样，对于STP6 - 120/36，RMSE值分别为1.660060E - 02、1.66006E - 02和1.66089E - 02。此外，对于PWP - 20，RMSE值分别为2.425075E - 03、2.42511E - 03和2.42510E - 03。
