文献类型：期刊文献

英文题名：Teaching-learning-based artificial bee colony for solar photovoltaic parameter estimation

作者：Chen, Xu[1,2];Xu, Bin[3];Mei, Congli[1];Ding, Yuhan[1];Li, Kangji[1]

机构：[1]Jiangsu Univ, Sch Elect & Informat Engn, Zhenjiang 212013, Jiangsu, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]Shanghai Univ Engn Sci, Sch Mech Engn, Shanghai 201620, Peoples R China

年份：2018

卷号：212

起止页码：1578

外文期刊名：APPLIED ENERGY

收录：;EI(收录号：20180304647202);WOS:【SCI-EXPANDED(收录号:WOS:000425200700117)】；

基金：This work was supported in part by the Natural Science Foundation of Jiangsu Province (Grant No. BK 20160540), the China Postdoctoral Science Foundation (Grant No. 2016M591783), the National Natural Science Foundation of China (Grant No. 61703268), the Research Talents Startup Foundation of Jiangsu University (Grant No. 15JDG139), the Fundamental Research Funds for the Central Universities (Grant No. 222201717006), and the PAPD of Jiangsu Higher Education Institutions.

语种：英文

外文关键词：Photovoltaic parameter estimation; Metaheuristic algorithm; Teaching-learning-based optimization; Artificial bee colony; Hybridization

摘要：Parameters estimation of photovoltaic (PV) model based on experimental data plays an important role in the simulation, evaluation, control, and optimization of PV systems. In the past decade, many metaheuristic algorithms have been used to extract the PV parameters; however, developing hybrid algorithms based on two or more metaheuristic algorithms may further improve the accuracy and reliability of single metaheuristic algorithms. In this paper, by combining teaching-learning-based optimization (TLBO) and artificial bee colony (ABC), we propose a new hybrid teaching-learning-based artificial bee colony (TLABC) for the solar PV parameter estimation problems. The proposed TLABC employs three hybrid search phases, namely teaching-based employed bee phase, learning-based on looker bee phase, and generalized oppositional scout bee phase to efficiently search the optimization parameters. TLABC is applied to identify parameters of different PV models, including single diode, double diode, and PV module, and the results of TLABC are compared with well-established TLBO and ABC algorithms, as well as those results reported in the literature. Experimental results show that TLABC can achieve superior performance in terms of accuracy and reliability for different PV parameter estimation problems.