The efficient identification of the unknown and changeable photovoltaic parameters is a matter of considerable interest to model photovoltaic systems. The accurate and efficient parameters are important in converting the entire photovoltaic system from solar to electricity. This paper, an ensemble multi strategy-driven shuffled frog leading algorithm (EMSFLA), is proposed to optimize photovoltaic modules' parameters and enhance solar energy conversion efficiency. In the EMSFLA, opposition-based learning can consider the opposite position in each frog memeplex to enhance the convergence velocity and keep the population diversity. The mutation and crossover operators abstracted from differential evolution with greedy strategy can better balance diversification and intensification during the optimization process. Then, the performance of the EMSFLA is preliminarily verified on representative benchmark functions compared to a slice of state-of-the-art algorithms. After that, the EMSFLA is employed to identify these parameters of single, double diode effectively, and photovoltaic modules thoroughly. Finally, the proposal's stability is further investigated on various temperatures and irradiation hierarchies on several manufacturers' datasheets. The outcome of statistical experiments has indicated that the EMSFLA performs higher accuracy and reliability in estimating photovoltaic mode's critical parameters, and it may be taken as a potential tool for parameter identification tasks in photovoltaic systems. For further info about this research, you can visit resources at https://aliasgharheidari.com.

对未知的和可变的光伏参数的有效识别是对光伏系统建模相当感兴趣的问题。准确高效的参数对于将整个光伏系统从太阳能转换为电能非常重要。本文提出了一种集成多策略驱动的混洗青蛙领先算法（EMSFLA），以优化光伏组件的参数并提高太阳能转换效率。在 EMSFLA 中，基于对立的学习可以考虑每个青蛙 memeplex 中的相反位置，以提高收敛速度并保持种群多样性。用贪婪策略从差分进化中抽象出来的变异和交叉算子可以在优化过程中更好地平衡多样化和集约化。然后，EMSFLA 的性能在有代表性的基准函数上得到了初步验证，与一些最先进的算法相比。之后，利用EMSFLA对单、双二极管的这些参数进行了有效的识别，并对光伏组件进行了彻底的识别。最后，在几个制造商的数据表上，在各种温度和辐照层次上进一步研究了该提案的稳定性。统计实验结果表明，EMSFLA在估计光伏模式的关键参数方面具有更高的准确性和可靠性，可作为光伏系统参数识别任务的潜在工具。有关此研究的更多信息，您可以访问 https://aliasgharheidari.com 上的资源。