An adaptive differential evolution with decomposition for photovoltaic parameter extraction
Photovoltaic (PV) parameter extraction plays a key role in establishing accurate and reliable PV models based on the manufacturer's current-voltage data. Owning to the characteristics such as implicit and nonlinear of the PV model, it remains a challenging and research-meaningful task in PV sys...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2021-08-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2021364?viewType=HTML |
| _version_ | 1818987195248148480 |
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| author | Zhen Yan Shuijia Li Wenyin Gong |
| author_facet | Zhen Yan Shuijia Li Wenyin Gong |
| author_sort | Zhen Yan |
| collection | DOAJ |
| description | Photovoltaic (PV) parameter extraction plays a key role in establishing accurate and reliable PV models based on the manufacturer's current-voltage data. Owning to the characteristics such as implicit and nonlinear of the PV model, it remains a challenging and research-meaningful task in PV system optimization. Despite there are many methods that have been developed to solve this problem, they are often consuming a great deal of computing resources for more satisfactory results. To reduce computing resources, in this paper, an advanced differential evolution with search space decomposition is developed to effectively extract the unknown parameters of PV models. In proposed approach, a recently proposed advanced differential evolution algorithm is used as a solver. In addition, a search space decomposition technique is introduced to reduce the dimension of the problem, thereby reducing the complexity of the problem. Three different PV cell models are selected for verifying the performance of proposed approach. The experimental result is firstly compared with some representative differential evolution algorithms that do not use search space decomposition technique, which demonstrates the effectiveness of the search space decomposition. Moreover, the comparison results with some reported well-established parameter extraction methods suggest that the proposed approach not only obtains accurate and reliable parameters, but also uses the least computational resources. |
| first_indexed | 2024-12-20T19:02:49Z |
| format | Article |
| id | doaj.art-9ef60666bd914cf99b4465d130b9aa47 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-20T19:02:49Z |
| publishDate | 2021-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-9ef60666bd914cf99b4465d130b9aa472022-12-21T19:29:22ZengAIMS PressMathematical Biosciences and Engineering1551-00182021-08-011867363738810.3934/mbe.2021364An adaptive differential evolution with decomposition for photovoltaic parameter extractionZhen Yan 0Shuijia Li1Wenyin Gong2School of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaSchool of Computer Science, China University of Geosciences, Wuhan 430074, ChinaPhotovoltaic (PV) parameter extraction plays a key role in establishing accurate and reliable PV models based on the manufacturer's current-voltage data. Owning to the characteristics such as implicit and nonlinear of the PV model, it remains a challenging and research-meaningful task in PV system optimization. Despite there are many methods that have been developed to solve this problem, they are often consuming a great deal of computing resources for more satisfactory results. To reduce computing resources, in this paper, an advanced differential evolution with search space decomposition is developed to effectively extract the unknown parameters of PV models. In proposed approach, a recently proposed advanced differential evolution algorithm is used as a solver. In addition, a search space decomposition technique is introduced to reduce the dimension of the problem, thereby reducing the complexity of the problem. Three different PV cell models are selected for verifying the performance of proposed approach. The experimental result is firstly compared with some representative differential evolution algorithms that do not use search space decomposition technique, which demonstrates the effectiveness of the search space decomposition. Moreover, the comparison results with some reported well-established parameter extraction methods suggest that the proposed approach not only obtains accurate and reliable parameters, but also uses the least computational resources.https://www.aimspress.com/article/doi/10.3934/mbe.2021364?viewType=HTMLparameter extractionphotovoltaic modeldecompositiondifferential evolutionadaptation |
| spellingShingle | Zhen Yan Shuijia Li Wenyin Gong An adaptive differential evolution with decomposition for photovoltaic parameter extraction Mathematical Biosciences and Engineering parameter extraction photovoltaic model decomposition differential evolution adaptation |
| title | An adaptive differential evolution with decomposition for photovoltaic parameter extraction |
| title_full | An adaptive differential evolution with decomposition for photovoltaic parameter extraction |
| title_fullStr | An adaptive differential evolution with decomposition for photovoltaic parameter extraction |
| title_full_unstemmed | An adaptive differential evolution with decomposition for photovoltaic parameter extraction |
| title_short | An adaptive differential evolution with decomposition for photovoltaic parameter extraction |
| title_sort | adaptive differential evolution with decomposition for photovoltaic parameter extraction |
| topic | parameter extraction photovoltaic model decomposition differential evolution adaptation |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2021364?viewType=HTML |
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