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.
Xu et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: