文献类型：期刊文献

英文题名：Parameters identification of solar cell models using generalized oppositional teaching learning based optimization

作者：Chen, Xu[1];Yu, Kunjie[2];Du, Wenli[2];Zhao, Wenxiang[1];Liu, Guohai[1]

机构：[1]Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Peoples R China;[2]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2016

卷号：99

起止页码：170

外文期刊名：ENERGY

收录：;EI(收录号：20161502224280);WOS:【SCI-EXPANDED(收录号:WOS:000374800300017)】；

基金：The authors would like to acknowledge the supports by the Research Talents Startup Foundation of Jiangsu University (Grant No. 15JDG139), the National Natural Science Foundation of China (Grant No. 51422702), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

语种：英文

外文关键词：Solar cell models; Parameter identification; Teaching learning based optimization; Generalized opposition-based learning

摘要：This paper presents a new optimization method called GOTLBO (generalized oppositional teaching learning based optimization) to identify parameters of solar cell models. GOTLBO employs generalized opposition-based learning to basic teaching learning based optimization through the initialization step and generation jumping so that the convergence speed is enhanced. The performance of GOTLBO is comprehensively evaluated in thirteen benchmark functions and two parameter identification problems of solar cell models, i.e., single diode model and double diode model. Simulation results indicate the excellent performance of GOTLBO compared with four well-known evolutionary algorithms and other parameter extraction techniques proposed in the literature. (C) 2016 Elsevier Ltd. All rights reserved.