A grouping strategy for reinforcement learning-based collective yaw control of wind farms
Reinforcement learning (RL) algorithms are expected to become the next generation of wind farm control methods. However, as wind farms continue to grow in size, the computational complexity of collective wind farm control will exponentially increase with the growth of action and state spaces, limiti...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-01-01
|
| Series: | Theoretical and Applied Mechanics Letters |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095034924000023 |
| _version_ | 1797353604758110208 |
|---|---|
| author | Chao Li Luoqin Liu Xiyun Lu |
| author_facet | Chao Li Luoqin Liu Xiyun Lu |
| author_sort | Chao Li |
| collection | DOAJ |
| description | Reinforcement learning (RL) algorithms are expected to become the next generation of wind farm control methods. However, as wind farms continue to grow in size, the computational complexity of collective wind farm control will exponentially increase with the growth of action and state spaces, limiting its potential in practical applications. In this Letter, we employ a RL-based wind farm control approach with multi-agent deep deterministic policy gradient to optimize the yaw manoeuvre of grouped wind turbines in wind farms. To reduce the computational complexity, the turbines in the wind farm are grouped according to the strength of the wake interaction. Meanwhile, to improve the control efficiency, each subgroup is treated as a whole and controlled by a single agent. Optimized results show that the proposed method can not only increase the power production of the wind farm but also significantly improve the control efficiency. |
| first_indexed | 2024-03-08T13:33:26Z |
| format | Article |
| id | doaj.art-6f9e879581e843a0aa7d95cf9fd71b04 |
| institution | Directory Open Access Journal |
| issn | 2095-0349 |
| language | English |
| last_indexed | 2024-03-08T13:33:26Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Theoretical and Applied Mechanics Letters |
| spelling | doaj.art-6f9e879581e843a0aa7d95cf9fd71b042024-01-17T04:16:06ZengElsevierTheoretical and Applied Mechanics Letters2095-03492024-01-01141100491A grouping strategy for reinforcement learning-based collective yaw control of wind farmsChao Li0Luoqin Liu1Xiyun Lu2Department of Modern Mechanics, University of Science and Technology of China, Hefei 230027, Anhui, PR ChinaCorresponding author.; Department of Modern Mechanics, University of Science and Technology of China, Hefei 230027, Anhui, PR ChinaDepartment of Modern Mechanics, University of Science and Technology of China, Hefei 230027, Anhui, PR ChinaReinforcement learning (RL) algorithms are expected to become the next generation of wind farm control methods. However, as wind farms continue to grow in size, the computational complexity of collective wind farm control will exponentially increase with the growth of action and state spaces, limiting its potential in practical applications. In this Letter, we employ a RL-based wind farm control approach with multi-agent deep deterministic policy gradient to optimize the yaw manoeuvre of grouped wind turbines in wind farms. To reduce the computational complexity, the turbines in the wind farm are grouped according to the strength of the wake interaction. Meanwhile, to improve the control efficiency, each subgroup is treated as a whole and controlled by a single agent. Optimized results show that the proposed method can not only increase the power production of the wind farm but also significantly improve the control efficiency.http://www.sciencedirect.com/science/article/pii/S2095034924000023Reinforcement learningWake steeringWind-farm flow controlProduction maximization |
| spellingShingle | Chao Li Luoqin Liu Xiyun Lu A grouping strategy for reinforcement learning-based collective yaw control of wind farms Theoretical and Applied Mechanics Letters Reinforcement learning Wake steering Wind-farm flow control Production maximization |
| title | A grouping strategy for reinforcement learning-based collective yaw control of wind farms |
| title_full | A grouping strategy for reinforcement learning-based collective yaw control of wind farms |
| title_fullStr | A grouping strategy for reinforcement learning-based collective yaw control of wind farms |
| title_full_unstemmed | A grouping strategy for reinforcement learning-based collective yaw control of wind farms |
| title_short | A grouping strategy for reinforcement learning-based collective yaw control of wind farms |
| title_sort | grouping strategy for reinforcement learning based collective yaw control of wind farms |
| topic | Reinforcement learning Wake steering Wind-farm flow control Production maximization |
| url | http://www.sciencedirect.com/science/article/pii/S2095034924000023 |
| work_keys_str_mv | AT chaoli agroupingstrategyforreinforcementlearningbasedcollectiveyawcontrolofwindfarms AT luoqinliu agroupingstrategyforreinforcementlearningbasedcollectiveyawcontrolofwindfarms AT xiyunlu agroupingstrategyforreinforcementlearningbasedcollectiveyawcontrolofwindfarms AT chaoli groupingstrategyforreinforcementlearningbasedcollectiveyawcontrolofwindfarms AT luoqinliu groupingstrategyforreinforcementlearningbasedcollectiveyawcontrolofwindfarms AT xiyunlu groupingstrategyforreinforcementlearningbasedcollectiveyawcontrolofwindfarms |