Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell Models
Extracting accurate values for relevant unknown parameters of solar cell models is vital and necessary for performance analysis of a photovoltaic (PV) system. This paper presents an effective application of a young, yet efficient metaheuristic, named the symbiotic organisms search (SOS) algorithm, f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/8/11/2155 |
| _version_ | 1818173720498274304 |
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| author | Guojiang Xiong Jing Zhang Xufeng Yuan Dongyuan Shi Yu He |
| author_facet | Guojiang Xiong Jing Zhang Xufeng Yuan Dongyuan Shi Yu He |
| author_sort | Guojiang Xiong |
| collection | DOAJ |
| description | Extracting accurate values for relevant unknown parameters of solar cell models is vital and necessary for performance analysis of a photovoltaic (PV) system. This paper presents an effective application of a young, yet efficient metaheuristic, named the symbiotic organisms search (SOS) algorithm, for the parameter extraction of solar cell models. SOS, inspired by the symbiotic interaction ways employed by organisms to improve their overall competitiveness in the ecosystem, possesses some noticeable merits such as being free from tuning algorithm-specific parameters, good equilibrium between exploration and exploitation, and being easy to implement. Three test cases including the single diode model, double diode model, and PV module model are served to validate the effectiveness of SOS. On one hand, the performance of SOS is evaluated by five state-of-the-art algorithms. On the other hand, it is also compared with some well-designed parameter extraction methods. Experimental results in terms of the final solution quality, convergence rate, robustness, and statistics fully indicate that SOS is very effective and competitive. |
| first_indexed | 2024-12-11T19:32:59Z |
| format | Article |
| id | doaj.art-8f775aa778f1432d8583974525b2956d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-12-11T19:32:59Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8f775aa778f1432d8583974525b2956d2022-12-22T00:53:14ZengMDPI AGApplied Sciences2076-34172018-11-01811215510.3390/app8112155app8112155Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell ModelsGuojiang Xiong0Jing Zhang1Xufeng Yuan2Dongyuan Shi3Yu He4Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaGuizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaGuizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaGuizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaExtracting accurate values for relevant unknown parameters of solar cell models is vital and necessary for performance analysis of a photovoltaic (PV) system. This paper presents an effective application of a young, yet efficient metaheuristic, named the symbiotic organisms search (SOS) algorithm, for the parameter extraction of solar cell models. SOS, inspired by the symbiotic interaction ways employed by organisms to improve their overall competitiveness in the ecosystem, possesses some noticeable merits such as being free from tuning algorithm-specific parameters, good equilibrium between exploration and exploitation, and being easy to implement. Three test cases including the single diode model, double diode model, and PV module model are served to validate the effectiveness of SOS. On one hand, the performance of SOS is evaluated by five state-of-the-art algorithms. On the other hand, it is also compared with some well-designed parameter extraction methods. Experimental results in terms of the final solution quality, convergence rate, robustness, and statistics fully indicate that SOS is very effective and competitive.https://www.mdpi.com/2076-3417/8/11/2155solar photovoltaicparameter extractionsymbiotic organisms searchmetaheuristic |
| spellingShingle | Guojiang Xiong Jing Zhang Xufeng Yuan Dongyuan Shi Yu He Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell Models Applied Sciences solar photovoltaic parameter extraction symbiotic organisms search metaheuristic |
| title | Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell Models |
| title_full | Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell Models |
| title_fullStr | Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell Models |
| title_full_unstemmed | Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell Models |
| title_short | Application of Symbiotic Organisms Search Algorithm for Parameter Extraction of Solar Cell Models |
| title_sort | application of symbiotic organisms search algorithm for parameter extraction of solar cell models |
| topic | solar photovoltaic parameter extraction symbiotic organisms search metaheuristic |
| url | https://www.mdpi.com/2076-3417/8/11/2155 |
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