Balancing Exploration–Exploitation of Multi-verse Optimizer for Parameter Extraction on Photovoltaic Models  

作　　者：Yan Han Weibin Chen Ali Asghar Heidari Huiling Chen Xin Zhang 

机构地区：[1]College of Computer Science and Artificial Intelligence,Wenzhou University,Wenzhou,325035,China [2]School of Surveying and Geospatial Engineering,College of Engineering,University of Tehran,Tehran,Iran [3]School of Biomedical Engineering,National Engineering Research Center of Ophthalmology and Optometry,Eye Hospital,Wenzhou Medical University,Wenzhou,325027,China [4]Key Lab of Biohealth Materials and Chemistry of Wenzhou,Wenzhou University,Wenzhou,China [5]Key Laboratory of Intelligent Informatics for Safety&Emergency of Zhejiang Province,Wenzhou University,Wenzhou,China

出　　处：《Journal of Bionic Engineering》2024年第2期1022-1054,共33页仿生工程学报（英文版）

基　　金：supported by the Natural Science Foundation of Zhejiang Province(LY21F020001,LZ22F020005);National Natural Science Foundation of China(62076185);Science and Technology Plan Project of Wenzhou,China(ZG2020026).

摘　　要：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.

关 键 词：Photovoltaic models Multi-verse optimizer Nelder-Mead simplex Iterative chaos map 

分 类 号：Q81[生物学—生物工程]