Multicluster Distributed Optimization Strategy for Turbine Wake Environment
As a crucial component of modern energy systems, wind energy plays a significant role in energy transition. In traditional wind power systems, mutual interference between wind turbines leads to wake effect, adversely impacts the power generation efficiency of wind farms. Herein, a multicluster distr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-08-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400884 |
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| _version_ | 1849230108524544000 |
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| author | Zhenping Yu Xinmeng Zhou Yedong Huang Kunyu Zhou Guangming Cui Juntian Qu |
| author_facet | Zhenping Yu Xinmeng Zhou Yedong Huang Kunyu Zhou Guangming Cui Juntian Qu |
| author_sort | Zhenping Yu |
| collection | DOAJ |
| description | As a crucial component of modern energy systems, wind energy plays a significant role in energy transition. In traditional wind power systems, mutual interference between wind turbines leads to wake effect, adversely impacts the power generation efficiency of wind farms. Herein, a multicluster distributed optimization strategy based on wake‐DBSCAN, specifically designed for environments affected by wake interference is proposed. First, clustering analysis on the wind turbine layout and wind conditions to establish a foundation for efficient distributed computation is performed. Based on the clustering results, wake analysis is conducted to plan and optimize the operational strategy for each wind turbine cluster, resulting in a distributed optimization strategy for their operation. Additionally, simulation experiments are conducted on micrositing and time‐varying wind conditions using real‐world data from a wind farm in the Arua region. The experimental results demonstrate that the proposed algorithm can effectively improve the computational efficiency of wake optimization, while ensuring the effect of the wake optimization algorithm in actual wind farms as much as possible, which is more in line with the wake optimization needs of actual wind farms. The algorithm proposed in this article provides valuable insights into wind turbine operation and maintenance under time‐varying conditions. |
| format | Article |
| id | doaj-art-b12f83c663eb40e094e9b5ae9e047a06 |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-b12f83c663eb40e094e9b5ae9e047a062025-08-21T11:05:47ZengWileyAdvanced Intelligent Systems2640-45672025-08-0178n/an/a10.1002/aisy.202400884Multicluster Distributed Optimization Strategy for Turbine Wake EnvironmentZhenping Yu0Xinmeng Zhou1Yedong Huang2Kunyu Zhou3Guangming Cui4Juntian Qu5Shenzhen International Graduate School Tsinghua University Shenzhen 518055 ChinaSchool of Electrical Engineering Huazhong University of Science and Technology Wuhan 430074 ChinaShenzhen International Graduate School Tsinghua University Shenzhen 518055 ChinaShenzhen International Graduate School Tsinghua University Shenzhen 518055 ChinaShenzhen International Graduate School Tsinghua University Shenzhen 518055 ChinaShenzhen International Graduate School Tsinghua University Shenzhen 518055 ChinaAs a crucial component of modern energy systems, wind energy plays a significant role in energy transition. In traditional wind power systems, mutual interference between wind turbines leads to wake effect, adversely impacts the power generation efficiency of wind farms. Herein, a multicluster distributed optimization strategy based on wake‐DBSCAN, specifically designed for environments affected by wake interference is proposed. First, clustering analysis on the wind turbine layout and wind conditions to establish a foundation for efficient distributed computation is performed. Based on the clustering results, wake analysis is conducted to plan and optimize the operational strategy for each wind turbine cluster, resulting in a distributed optimization strategy for their operation. Additionally, simulation experiments are conducted on micrositing and time‐varying wind conditions using real‐world data from a wind farm in the Arua region. The experimental results demonstrate that the proposed algorithm can effectively improve the computational efficiency of wake optimization, while ensuring the effect of the wake optimization algorithm in actual wind farms as much as possible, which is more in line with the wake optimization needs of actual wind farms. The algorithm proposed in this article provides valuable insights into wind turbine operation and maintenance under time‐varying conditions.https://doi.org/10.1002/aisy.202400884distributed algorithmsintelligent systemwake optimizationwind farm |
| spellingShingle | Zhenping Yu Xinmeng Zhou Yedong Huang Kunyu Zhou Guangming Cui Juntian Qu Multicluster Distributed Optimization Strategy for Turbine Wake Environment Advanced Intelligent Systems distributed algorithms intelligent system wake optimization wind farm |
| title | Multicluster Distributed Optimization Strategy for Turbine Wake Environment |
| title_full | Multicluster Distributed Optimization Strategy for Turbine Wake Environment |
| title_fullStr | Multicluster Distributed Optimization Strategy for Turbine Wake Environment |
| title_full_unstemmed | Multicluster Distributed Optimization Strategy for Turbine Wake Environment |
| title_short | Multicluster Distributed Optimization Strategy for Turbine Wake Environment |
| title_sort | multicluster distributed optimization strategy for turbine wake environment |
| topic | distributed algorithms intelligent system wake optimization wind farm |
| url | https://doi.org/10.1002/aisy.202400884 |
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