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...
Uloženo v:
| Vydáno v: | Applied energy Ročník 212; s. 1578 - 1588 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
15.02.2018
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| Témata: | |
| ISSN: | 0306-2619, 1872-9118 |
| On-line přístup: | Získat plný text |
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| Abstract | •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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| AbstractList | 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. •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. |
| Author | Xu, Bin Mei, Congli Chen, Xu Ding, Yuhan Li, Kangji |
| Author_xml | – sequence: 1 givenname: Xu orcidid: 0000-0003-2779-9978 surname: Chen fullname: Chen, Xu email: xuchen@ujs.edu.cn organization: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China – sequence: 2 givenname: Bin surname: Xu fullname: Xu, Bin organization: School of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China – sequence: 3 givenname: Congli surname: Mei fullname: Mei, Congli organization: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China – sequence: 4 givenname: Yuhan surname: Ding fullname: Ding, Yuhan organization: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China – sequence: 5 givenname: Kangji surname: Li fullname: Li, Kangji organization: School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China |
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| Title | Teaching–learning–based artificial bee colony for solar photovoltaic parameter estimation |
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