Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification
The whale optimization algorithm (WOA) is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA (MWOA) in wh...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/8878686 |
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| _version_ | 1832554574650540032 |
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| author | Xiaojia Ye Wei Liu Hong Li Mingjing Wang Chen Chi Guoxi Liang Huiling Chen Hailong Huang |
| author_facet | Xiaojia Ye Wei Liu Hong Li Mingjing Wang Chen Chi Guoxi Liang Huiling Chen Hailong Huang |
| author_sort | Xiaojia Ye |
| collection | DOAJ |
| description | The whale optimization algorithm (WOA) is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA (MWOA) in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the local optimum and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model (SDM), double diode model (DDM), and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules. |
| format | Article |
| id | doaj-art-7e48a433d48f4a18b8409f9e59109443 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7e48a433d48f4a18b8409f9e591094432025-02-03T05:51:12ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/88786868878686Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter IdentificationXiaojia Ye0Wei Liu1Hong Li2Mingjing Wang3Chen Chi4Guoxi Liang5Huiling Chen6Hailong Huang7Shanghai Lixin University of Accounting and Finance, Shanghai 201209, ChinaDepartment of Mathematics and Statistics, York University, CanadaWenzhou Vocational College of Science and Technology, Wenzhou, Zhejiang 325006, ChinaInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamOujiang College, Wenzhou University, Wenzhou 325035, Zhejiang, ChinaDepartment of Information Technology, Wenzhou Polytechnic, Wenzhou 325035, ChinaDepartment of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, Zhejiang, ChinaDepartment of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, Zhejiang, ChinaThe whale optimization algorithm (WOA) is a powerful swarm intelligence method which has been widely used in various fields such as parameter identification of solar cells and PV modules. In order to better balance the exploration and exploitation of WOA, we propose a novel modified WOA (MWOA) in which both the mutation strategy based on Levy flight and a local search mechanism of pattern search are introduced. On the one hand, Levy flight can make the algorithm get rid of the local optimum and avoid stagnation; thus, it is able to prevent the algorithm from losing diversity and to increase the global search capability. On the other hand, pattern search, a direct search method, has not only high convergence rate but also good stability, which can boost the local optimization ability of the WOA. Therefore, the combination of these two mechanisms can greatly improve the capability of WOA to obtain the best solution. In addition, MWOA may be employed to estimate parameters in single diode model (SDM), double diode model (DDM), and PV modules and to identify unknown parameters of two different types of PV modules under diverse light irradiance and temperature conditions. The analytical results demonstrate the validity and the practicality of MWOA for estimating parameters of solar cells and PV modules.http://dx.doi.org/10.1155/2021/8878686 |
| spellingShingle | Xiaojia Ye Wei Liu Hong Li Mingjing Wang Chen Chi Guoxi Liang Huiling Chen Hailong Huang Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification Complexity |
| title | Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification |
| title_full | Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification |
| title_fullStr | Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification |
| title_full_unstemmed | Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification |
| title_short | Modified Whale Optimization Algorithm for Solar Cell and PV Module Parameter Identification |
| title_sort | modified whale optimization algorithm for solar cell and pv module parameter identification |
| url | http://dx.doi.org/10.1155/2021/8878686 |
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