Photovoltaic Global Maximum Power Tracking Based on Improved Dragonfly Algorithm
A multi-peak phenomenon can be observed on the power-voltage (P-U) characteristic curve of a photovoltaic (PV) array under partially shaded conditions (PSCs). In this case, conventional maximum power point tracking (MPPT) algorithms tend to fall into local extremums, and swarm intelligence algorithm...
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2022-02-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202010137 |
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| _version_ | 1850069253098569728 |
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| author | Fei XUE Xin MA Bei TIAN Hui WU |
| author_facet | Fei XUE Xin MA Bei TIAN Hui WU |
| author_sort | Fei XUE |
| collection | DOAJ |
| description | A multi-peak phenomenon can be observed on the power-voltage (P-U) characteristic curve of a photovoltaic (PV) array under partially shaded conditions (PSCs). In this case, conventional maximum power point tracking (MPPT) algorithms tend to fall into local extremums, and swarm intelligence algorithms would spend much time in tracking. Thus, this paper proposes an improved MPPT algorithm based on the dragonfly algorithm (DA) and the perturbation and observation (P & O) algorithm. The convergence rate and global search ability of the algorithm are improved by optimizing particle roles and introducing the Lévy flight model. With the P & O algorithm, the concept of population density is put forward and an optimal local search strategy is formulated to modify the population search efficiency and precision. Finally, comparisons with the P & O algorithm, particle swarm optimization (PSO) algorithm, and the original DA through simulation verify the validity of the proposed algorithm. |
| format | Article |
| id | doaj-art-55a0d90f140f454ebbd6487a2f709de0 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2022-02-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-55a0d90f140f454ebbd6487a2f709de02025-08-20T02:47:49ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492022-02-0155213113710.11930/j.issn.1004-9649.202010137zgdl-54-12-xuefeiPhotovoltaic Global Maximum Power Tracking Based on Improved Dragonfly AlgorithmFei XUE0Xin MA1Bei TIAN2Hui WU3Electric Power Research Institute, State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750002, ChinaElectric Power Research Institute, State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750002, ChinaElectric Power Research Institute, State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750002, ChinaElectric Power Research Institute, State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750002, ChinaA multi-peak phenomenon can be observed on the power-voltage (P-U) characteristic curve of a photovoltaic (PV) array under partially shaded conditions (PSCs). In this case, conventional maximum power point tracking (MPPT) algorithms tend to fall into local extremums, and swarm intelligence algorithms would spend much time in tracking. Thus, this paper proposes an improved MPPT algorithm based on the dragonfly algorithm (DA) and the perturbation and observation (P & O) algorithm. The convergence rate and global search ability of the algorithm are improved by optimizing particle roles and introducing the Lévy flight model. With the P & O algorithm, the concept of population density is put forward and an optimal local search strategy is formulated to modify the population search efficiency and precision. Finally, comparisons with the P & O algorithm, particle swarm optimization (PSO) algorithm, and the original DA through simulation verify the validity of the proposed algorithm.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202010137photovoltaic systemmaximum power point trackingmulti-peak characteristicdragonfly algorithmperturbation and observation algorithm |
| spellingShingle | Fei XUE Xin MA Bei TIAN Hui WU Photovoltaic Global Maximum Power Tracking Based on Improved Dragonfly Algorithm Zhongguo dianli photovoltaic system maximum power point tracking multi-peak characteristic dragonfly algorithm perturbation and observation algorithm |
| title | Photovoltaic Global Maximum Power Tracking Based on Improved Dragonfly Algorithm |
| title_full | Photovoltaic Global Maximum Power Tracking Based on Improved Dragonfly Algorithm |
| title_fullStr | Photovoltaic Global Maximum Power Tracking Based on Improved Dragonfly Algorithm |
| title_full_unstemmed | Photovoltaic Global Maximum Power Tracking Based on Improved Dragonfly Algorithm |
| title_short | Photovoltaic Global Maximum Power Tracking Based on Improved Dragonfly Algorithm |
| title_sort | photovoltaic global maximum power tracking based on improved dragonfly algorithm |
| topic | photovoltaic system maximum power point tracking multi-peak characteristic dragonfly algorithm perturbation and observation algorithm |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202010137 |
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