文献类型：期刊文献

英文题名：Multiple learning backtracking search algorithm for estimating parameters of photovoltaic models

作者：Yu, Kunjie[1,2];Liang, J. J.[1];Qu, B. Y.[3];Cheng, Zhiping[1];Wang, Heshan[1]

机构：[1]Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, Peoples R China;[2]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[3]Zhongyuan Univ Technol, Sch Elect & Informat Engn, Zhengzhou 450007, Henan, Peoples R China

年份：2018

卷号：226

起止页码：408

外文期刊名：APPLIED ENERGY

收录：;EI(收录号：20182405301674);WOS:【SCI-EXPANDED(收录号:WOS:000441688100033)】；

基金：This work was supported by the National Natural Science Foundation of China (61473266, 61673404, and 61603343), China Postdoctoral Science Foundation (2017M622373), Fundamental Research Funds for the Central Universities (222201817006), Program for Science & Technology Innovation Talents in Universities of Henan Province (16HASTIT041, 16HASTIT033), China Textile Industry Association Science and Technology Guidance Project (2017054), Young Backbone Teachers of Henan Province (2016GGJS-094), and Key Projects of Higher Education of Henan Province (16A120018, 17A120014, and 18A470017).

语种：英文

外文关键词：Parameter identification; Photovoltaic model; Backtracking search algorithm; Multiple learning

摘要：Obtaining appropriate parameters of photovoltaic models based on measured current-voltage data is crucial for the evaluation, control, and optimization of photovoltaic systems. Although many techniques have been developed to solve this problem, it is still challenging to identify the model parameters accurately and reliably. To improve parameters identification of different photovoltaic models, a multiple learning backtracking search algorithm (MLBSA) is proposed in this paper. In MLBSA, some individuals learn from the current population information and historical population information simultaneously, which aims to maintain population diversity and enhance the exploration ability. While other individuals learn from the best individual of current population to improve the convergence speed and thus enhance the exploitation ability. In addition, an elite strategy based on chaotic local search is developed to further refine the quality of current population. The proposed MLBSA is employed to solve the parameters identification problems of different photovoltaic models, i.e., single diode, double diode, and photovoltaic module. Comprehensive experimental results and analyses demonstrate that MLBSA outperforms other state-of-the-art algorithms in terms of accuracy, reliability, and computational efficiency.