An efficient teaching-learning-based optimization algorithm for parameters identification of photovoltaic models: Analysis and validations
•A meta-heuristic is proposed to identify unknown parameters of photovoltaic model.•To that end, a modified teaching learning-based optimization approach is designed.•Three commercial photovoltaic models are evaluated using one and two diode models.•Intensive verifications with other competing metho...
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| Vydané v: | Energy conversion and management Ročník 227; s. 113614 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Oxford
Elsevier Ltd
01.01.2021
Elsevier Science Ltd |
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| ISSN: | 0196-8904, 1879-2227 |
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| Abstract | •A meta-heuristic is proposed to identify unknown parameters of photovoltaic model.•To that end, a modified teaching learning-based optimization approach is designed.•Three commercial photovoltaic models are evaluated using one and two diode models.•Intensive verifications with other competing methods are executed.•The accuracy, reliability, and convergence of the proposed algorithm are confirmed.
Accurate and efficient parameter estimation of the photovoltaic (PV) models is considered a dispensable process to simulate the PV systems. Therefore, many meta-heuristic algorithms have been recently proposed, but the parameters obtained are not as accurate and reliable as is desired, particularly when the PV models have a significant number of unknown parameters. Therefore, in this paper, a modified teaching–learning based optimization (MTLBO) approach is suggested to accurately and reliably extract the unknown parameters of PV models. Our modification to TLBO divides each of the teaching and learning phases into three levels: low, medium, and high according to the scoring level of each learner. The scoring level of each one is measured based on comparison between the fitness of the updated learner and the current leaner; if the fitness of the updated is better, the scoring level is reset to 0, and otherwise, it is increased by 1. Finally, to observe the efficacy of MTLBO, it is investigated on five PV cells and modules: single diode model and double diode model in case of RTC France, Photowatt-PWP201 module, STM6-40/36 module, and STP6-120/36 module. For those PV cells and modules, our proposed could respectively come true the following average outcomes: 0.0009860219, 0.0009825026, 0.0024250749, 0.0017298137, and 0.0166006031. To check the efficacy of MTLBO, it is compared with a number of recent and well-known algorithms. The experimental results show the superiority of the proposed algorithm, especially on double diode model. |
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| AbstractList | Accurate and efficient parameter estimation of the photovoltaic (PV) models is considered a dispensable process to simulate the PV systems. Therefore, many meta-heuristic algorithms have been recently proposed, but the parameters obtained are not as accurate and reliable as is desired, particularly when the PV models have a significant number of unknown parameters. Therefore, in this paper, a modified teaching–learning based optimization (MTLBO) approach is suggested to accurately and reliably extract the unknown parameters of PV models. Our modification to TLBO divides each of the teaching and learning phases into three levels: low, medium, and high according to the scoring level of each learner. The scoring level of each one is measured based on comparison between the fitness of the updated learner and the current leaner; if the fitness of the updated is better, the scoring level is reset to 0, and otherwise, it is increased by 1. Finally, to observe the efficacy of MTLBO, it is investigated on five PV cells and modules: single diode model and double diode model in case of RTC France, Photowatt-PWP201 module, STM6-40/36 module, and STP6-120/36 module. For those PV cells and modules, our proposed could respectively come true the following average outcomes: 0.0009860219, 0.0009825026, 0.0024250749, 0.0017298137, and 0.0166006031. To check the efficacy of MTLBO, it is compared with a number of recent and well-known algorithms. The experimental results show the superiority of the proposed algorithm, especially on double diode model. •A meta-heuristic is proposed to identify unknown parameters of photovoltaic model.•To that end, a modified teaching learning-based optimization approach is designed.•Three commercial photovoltaic models are evaluated using one and two diode models.•Intensive verifications with other competing methods are executed.•The accuracy, reliability, and convergence of the proposed algorithm are confirmed. Accurate and efficient parameter estimation of the photovoltaic (PV) models is considered a dispensable process to simulate the PV systems. Therefore, many meta-heuristic algorithms have been recently proposed, but the parameters obtained are not as accurate and reliable as is desired, particularly when the PV models have a significant number of unknown parameters. Therefore, in this paper, a modified teaching–learning based optimization (MTLBO) approach is suggested to accurately and reliably extract the unknown parameters of PV models. Our modification to TLBO divides each of the teaching and learning phases into three levels: low, medium, and high according to the scoring level of each learner. The scoring level of each one is measured based on comparison between the fitness of the updated learner and the current leaner; if the fitness of the updated is better, the scoring level is reset to 0, and otherwise, it is increased by 1. Finally, to observe the efficacy of MTLBO, it is investigated on five PV cells and modules: single diode model and double diode model in case of RTC France, Photowatt-PWP201 module, STM6-40/36 module, and STP6-120/36 module. For those PV cells and modules, our proposed could respectively come true the following average outcomes: 0.0009860219, 0.0009825026, 0.0024250749, 0.0017298137, and 0.0166006031. To check the efficacy of MTLBO, it is compared with a number of recent and well-known algorithms. The experimental results show the superiority of the proposed algorithm, especially on double diode model. |
| ArticleNumber | 113614 |
| Author | Mohamed, Reda Ryan, Michael J. Sallam, Karam Abdel-Basset, Mohamed Chakrabortty, Ripon K. |
| Author_xml | – sequence: 1 givenname: Mohamed surname: Abdel-Basset fullname: Abdel-Basset, Mohamed email: mohamedbasset@zu.edu.eg organization: Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah 44519, Egypt – sequence: 2 givenname: Reda surname: Mohamed fullname: Mohamed, Reda email: redamoh@zu.edu.eg organization: Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah 44519, Egypt – sequence: 3 givenname: Ripon K. orcidid: 0000-0002-7373-0149 surname: Chakrabortty fullname: Chakrabortty, Ripon K. email: r.chakrabortty@adfa.edu.au organization: Capability Systems Centre, School of Engineering and IT, UNSW Canberra, Australia – sequence: 4 givenname: Karam surname: Sallam fullname: Sallam, Karam email: karam_sallam@zu.edu.eg organization: Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah 44519, Egypt – sequence: 5 givenname: Michael J. surname: Ryan fullname: Ryan, Michael J. email: m.ryan@adfa.edu.au organization: Capability Systems Centre, School of Engineering and IT, UNSW Canberra, Australia |
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| Snippet | •A meta-heuristic is proposed to identify unknown parameters of photovoltaic model.•To that end, a modified teaching learning-based optimization approach is... Accurate and efficient parameter estimation of the photovoltaic (PV) models is considered a dispensable process to simulate the PV systems. Therefore, many... |
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| SubjectTerms | administrative management Algorithms cells diodes energy conversion Environmental factors estimation Fitness France Heuristic methods Learning Machine learning Mathematical models Modules Optimization Parameter estimation Parameter identification Parameter modification Photovoltaic cells Photovoltaics PV models solar collectors Solar energy Teaching-learning |
| Title | An efficient teaching-learning-based optimization algorithm for parameters identification of photovoltaic models: Analysis and validations |
| URI | https://dx.doi.org/10.1016/j.enconman.2020.113614 https://www.proquest.com/docview/2479060550 https://www.proquest.com/docview/2498295135 |
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