文献类型：期刊文献

英文题名：Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization

作者：Yu, Kunjie[1];Chen, Xu[2];Wang, Xin[3];Wang, Zhenlei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Jiangsu, Peoples R China;[3]Shanghai Jiao Tong Univ, Ctr Elect & Elect Technol, Shanghai 200240, Peoples R China

年份：2017

卷号：145

起止页码：233

外文期刊名：ENERGY CONVERSION AND MANAGEMENT

收录：;EI(收录号：20171903648001);WOS:【SCI-EXPANDED(收录号:WOS:000403624400020)】；

基金：This research was supported by the National Natural Science Foundation of China (61422303, 61590923, 61533003, 61673268), Shanghai Natural Science Foundation (14ZR1421800), State Key Laboratory of Synthetical Automation for Process Industries, Natural Science Foundation of Jiangsu Province (BK20160540), and Fundamental Research Funds for the Central Universities (222201717006).

语种：英文

外文关键词：Photovoltaic model; Parameter identification; Teaching-learning-based optimization; Learning strategy

摘要：Parameters identification of photovoltaic (PV) model based on measured current-voltage characteristic curves plays an important role in the simulation and evaluation of PV systems. To accurately and reliably identify the PV model parameters, a self-adaptive teaching-learning-based optimization (SATLBO) is proposed in this paper. In SATLBO, the learners can self-adaptively select different learning phases based on their knowledge level. The better learners are more likely to choose the learner phase for improving the population diversity, while the worse learners tend to choose the teacher phase to enhance the convergence rate. Thus, learners at different levels focus on different searching abilities to efficiently enhance the performance of algorithm. In addition, to improve the searching ability of different learning phases, an elite learning strategy and a diversity learning method are introduced into the teacher phase and learner phase, respectively. The performance of SATLBO is firstly evaluated on 34 benchmark functions, and experimental results show that SATLBO achieves the first in ranking on the overall performance among nine algorithms. Then, SATLBO is employed to identify parameters of different PV models, i.e., single diode, double diode, and PV module. Experimental results indicate that SATLBO exhibits high accuracy and reliability compared with other parameter extraction methods. (C) 2017 Elsevier Ltd. All rights reserved.