Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation

•Teaching-learning-based artificial bee colony algorithm is proposed.•Three hybrid teaching-learning-based bee search phases are presented.•The method is applied to solve three photovoltaic parameters estimation problems.•It achievesvery competitive results in terms of accuracy and reliability. Para...

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Bibliographic Details
Published in:Applied energy Vol. 212; pp. 1578 - 1588
Main Authors: Chen, Xu, Xu, Bin, Mei, Congli, Ding, Yuhan, Li, Kangji
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.02.2018
Subjects:
algorithms
Artificial bee colony
bees
diodes
Hybridization
Metaheuristic algorithm
Photovoltaic parameter estimation
solar collectors
Teaching-learning-based optimization
Artificial bee colony
Hybridization
Metaheuristic algorithm
Photovoltaic parameter estimation
Teaching-learning-based optimization
ISSN:0306-2619, 1872-9118
Online Access:Get full text
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Summary:•Teaching-learning-based artificial bee colony algorithm is proposed.•Three hybrid teaching-learning-based bee search phases are presented.•The method is applied to solve three photovoltaic parameters estimation problems.•It achievesvery competitive results in terms of accuracy and reliability. 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.
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ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2017.12.115