A multi-strategy improved nutcracker optimization algorithm for parameter extraction problems of solar photovoltaic cells
Yukun Wang, Haoran Chen, Wansheng Cheng
Abstract
Parameter extraction for solar photovoltaic (PV) cells represents a highly nonlinear and complex practical problem. Although various meta-heuristic algorithms have been extensively employed to extract parameters from PV models, the accuracy and reliability of the extracted results often fall short of expectations. To address this challenge, this paper proposes a multi-strategy enhanced variant named EMNOA. This variant integrates adaptive parameters, Cauchy perturbation, adaptive fitness update mechanism, and an information-sharing mechanism to mitigate the deficiencies of the nutcracker optimization algorithm (NOA), such as the imbalance between exploration and exploitation and the tendency to fall into local optima. The performance of EMNOA was evaluated and compared with several state-of-the-art meta-heuristic algorithms on CEC2013 and CEC2017 benchmark function suites. Finally, EMNOA was applied to extract parameters from four PV models. The experimental results demonstrate that EMNOA achieves improvements over NOA, not only in numerical optimization but also in PV parameter estimation. Across four distinct PV models, EMNOA reduces root mean square error (RMSE) by 29.16%, 25.45%, 37.35%, and 88.95% respectively compared to NOA. EMNOA serves as a robust and effective tool for addressing the parameter extraction problem in PV models.