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基于动态多维随机机制和Nelder-Mead单纯形法的RIME优化用于光伏参数估计

RIME optimization with dynamic multi-dimensional random mechanism and Nelder-Mead simplex for photovoltaic parameter estimation.

作者信息

Zheng Yuanping, Kuang Fangjun, Heidari Ali Asghar, Yuan Lipei, Zhang Siyang, Chen Huiling

机构信息

Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China.

School of Information Engineering, Wenzhou Business College, Wenzhou, 325035, China.

出版信息

Sci Rep. 2025 Jul 1;15(1):20951. doi: 10.1038/s41598-025-99105-6.

DOI:10.1038/s41598-025-99105-6
PMID:40595358
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12219663/
Abstract

Solar photovoltaic technology is efficient and clean, but extracting photovoltaic cell parameters is challenging due to various influencing factors. The rime optimization algorithm (RIME) is a recently proposed metaheuristic algorithm (MAs). This paper introduces the dynamic multi-dimensional random mechanism (DMRM) combined with the Nelder-Mead simplex (NMs) to propose an enhanced version of RIME, called DNMRIME. DMRM improves the convergence accuracy of RIME by random non-periodic convergence, and NMs accelerate convergence, enabling DNMRIME to escape local optima and perform better on hybrid and composite functions. To evaluate the performance of DNMRIME, a qualitative analysis and an ablation study were conducted on CEC 2017. To verify its effectiveness, DNMRIME was compared with 14 well-known MAs, including some champion algorithms, and the results of the Wilcoxon signed rank test showed that DNMRIME ranked first. To extract parameters on SDM, DDM, TDM, and PV, DNMRIME was applied, resulting in mean RMSE values of 9.8602188324E - 04, 9.8296993325E - 04, 9.8393451046E - 04, and 2.4250748704E - 03 respectively. Moreover, under varying temperature and irradiation conditions on three manufacturers (KC200GT, ST40, SM55), DNMRIME extracted parameters with simulation data matching the actual data. Therefore, unlike previous studies, this study proposes DMRM and DNMRIME, demonstrating the efficiency and practicality of DNMRIME and further highlighting potential value of DNMRIME in photovoltaic parameter extraction. The source code of DNMRIME is available at https://github.com/zyetpink/DNMRIME-Solar-Model-dataset .

摘要

太阳能光伏技术高效且清洁，但由于各种影响因素，提取光伏电池参数具有挑战性。rime优化算法（RIME）是最近提出的一种元启发式算法（MAs）。本文引入了动态多维随机机制（DMRM）并结合Nelder-Mead单纯形法（NMs），提出了RIME的增强版本，称为DNMRIME。DMRM通过随机非周期性收敛提高了RIME的收敛精度，NMs加速了收敛，使DNMRIME能够逃离局部最优，并在混合函数和复合函数上表现更好。为了评估DNMRIME的性能，对CEC 2017进行了定性分析和消融研究。为了验证其有效性，将DNMRIME与14种著名的MAs进行了比较，包括一些冠军算法，Wilcoxon符号秩检验结果表明DNMRIME排名第一。为了在SDM、DDM、TDM和PV上提取参数，应用了DNMRIME，得到的均方根误差（RMSE）平均值分别为9.8602188324E - 04、9.8296993325E - 04、9.8393451046E - 04和2.4250748704E - 03。此外，在三家制造商（KC200GT、ST40、SM55）不同的温度和光照条件下，DNMRIME提取的参数与模拟数据和实际数据相匹配。因此，与以往的研究不同，本研究提出了DMRM和DNMRIME，证明了DNMRIME的效率和实用性，并进一步突出了DNMRIME在光伏参数提取中的潜在价值。DNMRIME的源代码可在https://github.com/zyetpink/DNMRIME-Solar-Model-dataset获取。

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