Abstract

Extracting photovoltaic (PV) model parameters based on the measured voltage and current information is crucial in the simulation and management of PV systems. To accurately and reliably extract the unknown parameters of different PV models, this paper proposes an improved multi-verse optimizer that integrates an iterative chaos map and the Nelder–Mead simplex method, INMVO. Quantitative experiments verified that the proposed INMVO fueled by both mechanisms has more affluent populations and a more reasonable balance between exploration and exploitation. Further, to verify the feasibility and competitiveness of the proposal, this paper employed INMVO to extract the unknown parameters on single-diode, double-diode, three-diode, and PV module four well-known PV models, and the high-performance techniques are selected for comparison. In addition, the Wilcoxon signed-rank and Friedman tests were employed to test the experimental results statistically. Various evaluation metrics, such as root means square error, relative error, absolute error, and statistical test, demonstrate that the proposed INMVO works effectively and accurately to extract the unknown parameters on different PV models compared to other techniques. In addition, the capability of INMVO to stably and accurately extract unknown parameters was also verified on three commercial PV modules under different irradiance and temperatures. In conclusion, the proposal in this paper can be implemented as an advanced and reliable tool for extracting the unknown parameters of different PV models. Note that the source code of INMVO is available at https://github.com/woniuzuioupao/INMVO.

中文翻译：

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平衡探索——光伏模型参数提取的多维优化器的开发

摘要

根据测量的电压和电流信息提取光伏（PV）模型参数对于光伏系统的仿真和管理至关重要。为了准确可靠地提取不同光伏模型的未知参数，本文提出了一种改进的多维优化器，该优化器集成了迭代混沌图和Nelder-Mead单纯形方法INMVO。定量实验验证了两种机制推动的INMVO拥有更富裕的人口和更合理的勘探与开采之间的平衡。进一步，为了验证该方案的可行性和竞争力，本文采用INMVO提取了单二极管、双二极管、三二极管和光伏组件四种著名光伏模型的未知参数，并采用高性能技术被选中进行比较。此外，采用Wilcoxon符号秩和Friedman检验对实验结果进行统计检验。各种评估指标，如均方根误差、相对误差、绝对误差和统计测试，表明与其他技术相比，所提出的 INMVO 可以有效、准确地提取不同 PV 模型上的未知参数。此外，还在不同辐照度和温度下的三个商用光伏组件上验证了INMVO稳定、准确地提取未知参数的能力。总之，本文的建议可以作为一种先进可靠的工具来实现，用于提取不同光伏模型的未知参数。请注意，INMVO 的源代码可在 https://github.com/woniuzuioupao/INMVO 获取。