Yong Xu, Bicong Sang, Yi Zhang
College of Electrical and Computer Science, Jilin Jianzhu University, Changchun, China.
Sci Rep. 2025 Jul 31;15(1):27931. doi: 10.1038/s41598-025-04396-4.
This paper proposes an improved Pelican optimization algorithm (IPOA) based on comprehansive strategy for the parameter identification of photovoltaic models. Firstly, the cubic chaotic mapping and the refraction reverse learning strategy are used to initialize the pelican population and enhance its diversity. Secondly, the position update formula of the Pelican optimization algorithm in the global detection phase is replaced by the position update formula of the red-tailed Eagle optimization algorithm in the soaring phase to obtain the adequacy of the Pelican optimization algorithm in solution space search. Further introducing the catchy variation strategy aims to improve the algorithm's global search ability. Finally, the reverse solution generated by the lens imaging principle can provide a new search direction through the mirror reverse learning strategy when the Pelican optimization algorithm falls into the local optimal. The CEC2022 test function performed analysis and comparison with eight meta-heuristic algorithms. The Wilcoxon rank sum test verified the significance of the algorithm. In addition, the IPOA was used to optimize the critical parameters of the PV model to solve the problem of actual parameter identification of the single-diode and double-diode photovoltaic module models. The experimental results indicate that the IPOA outperforms other classical swarm intelligence algorithms in both convergence speed and solving accuracy. Furthermore, this optimization method yields the smallest mean square error across all types of solar cells, demonstrating the superiority of the proposed algorithm.
本文提出了一种基于综合策略的改进鹈鹕优化算法（IPOA），用于光伏模型的参数识别。首先，利用立方混沌映射和折射反向学习策略对鹈鹕种群进行初始化，并增强其多样性。其次，将鹈鹕优化算法在全局探测阶段的位置更新公式替换为红尾鹰优化算法在翱翔阶段的位置更新公式，以获得鹈鹕优化算法在解空间搜索中的充分性。进一步引入引人注目的变异策略旨在提高算法的全局搜索能力。最后，当鹈鹕优化算法陷入局部最优时，由透镜成像原理产生的反向解可以通过镜像反向学习策略提供一个新的搜索方向。利用CEC2022测试函数与八种元启发式算法进行了分析和比较。Wilcoxon秩和检验验证了该算法的显著性。此外，利用IPOA对光伏模型的关键参数进行优化，解决了单二极管和双二极管光伏模块模型的实际参数识别问题。实验结果表明，IPOA在收敛速度和求解精度方面均优于其他经典群智能算法。此外，这种优化方法在所有类型的太阳能电池中产生的均方误差最小，证明了所提算法的优越性。
