A Supervisory Control Framework for Fatigue-Aware Wake Steering in Wind Farms
Wake steering has emerged as a promising strategy to mitigate turbine wake losses, with existing research largely focusing on the aerodynamic optimization of yaw angles. However, many prior approaches rely on static look-up tables (LUTs), offering limited adaptability to real-world wind variability...
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2025-06-01
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| Online Access: | https://www.mdpi.com/1996-1073/18/13/3452 |
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| author | Yang Shen Jinkui Zhu Peng Hou Shuowang Zhang Xinglin Wang Guodong He Chao Lu Enyu Wang Yiwen Wu |
| author_facet | Yang Shen Jinkui Zhu Peng Hou Shuowang Zhang Xinglin Wang Guodong He Chao Lu Enyu Wang Yiwen Wu |
| author_sort | Yang Shen |
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| description | Wake steering has emerged as a promising strategy to mitigate turbine wake losses, with existing research largely focusing on the aerodynamic optimization of yaw angles. However, many prior approaches rely on static look-up tables (LUTs), offering limited adaptability to real-world wind variability and leading to non-optimal results. More importantly, these energy-focused strategies overlook the mechanical implications of frequent yaw activities in pursuit of the maximum power output, which may lead to premature exhaustion of the yaw system’s design life, thereby accelerating structural degradation. This study proposes a supervisory control framework that balances energy capture with structural reliability through three key innovations: (1) upstream-based inflow sensing for real-time capture of free-stream wind, (2) fatigue-responsive optimization constrained by a dynamic actuation quota system with adaptive yaw activation, and (3) a bidirectional threshold adjustment mechanism that redistributes unused actuation allowances and compensates for transient quota overruns. A case study at an offshore wind farm shows that the framework improves energy yield by 3.94%, which is only 0.29% below conventional optimization, while reducing yaw duration and activation frequency by 48.5% and 74.6%, respectively. These findings demonstrate the framework’s potential as a fatigue-aware control paradigm that balances energy efficiency with system longevity. |
| format | Article |
| id | doaj-art-eaf731fbb5334b849101cd1e0152f19f |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-eaf731fbb5334b849101cd1e0152f19f2025-08-20T02:35:54ZengMDPI AGEnergies1996-10732025-06-011813345210.3390/en18133452A Supervisory Control Framework for Fatigue-Aware Wake Steering in Wind FarmsYang Shen0Jinkui Zhu1Peng Hou2Shuowang Zhang3Xinglin Wang4Guodong He5Chao Lu6Enyu Wang7Yiwen Wu8Zhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, ChinaZhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, ChinaZhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, ChinaState Key Laboratory of Offshore Wind Power Equipment and Wind Energy High-Efficient Utilization, Xiangtan 411102, ChinaState Key Laboratory of Offshore Wind Power Equipment and Wind Energy High-Efficient Utilization, Xiangtan 411102, ChinaZhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, ChinaZhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, ChinaZhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, ChinaZhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, ChinaWake steering has emerged as a promising strategy to mitigate turbine wake losses, with existing research largely focusing on the aerodynamic optimization of yaw angles. However, many prior approaches rely on static look-up tables (LUTs), offering limited adaptability to real-world wind variability and leading to non-optimal results. More importantly, these energy-focused strategies overlook the mechanical implications of frequent yaw activities in pursuit of the maximum power output, which may lead to premature exhaustion of the yaw system’s design life, thereby accelerating structural degradation. This study proposes a supervisory control framework that balances energy capture with structural reliability through three key innovations: (1) upstream-based inflow sensing for real-time capture of free-stream wind, (2) fatigue-responsive optimization constrained by a dynamic actuation quota system with adaptive yaw activation, and (3) a bidirectional threshold adjustment mechanism that redistributes unused actuation allowances and compensates for transient quota overruns. A case study at an offshore wind farm shows that the framework improves energy yield by 3.94%, which is only 0.29% below conventional optimization, while reducing yaw duration and activation frequency by 48.5% and 74.6%, respectively. These findings demonstrate the framework’s potential as a fatigue-aware control paradigm that balances energy efficiency with system longevity.https://www.mdpi.com/1996-1073/18/13/3452wake steeringyaw optimizationwind farm supervisory controlfatigue-aware control |
| spellingShingle | Yang Shen Jinkui Zhu Peng Hou Shuowang Zhang Xinglin Wang Guodong He Chao Lu Enyu Wang Yiwen Wu A Supervisory Control Framework for Fatigue-Aware Wake Steering in Wind Farms Energies wake steering yaw optimization wind farm supervisory control fatigue-aware control |
| title | A Supervisory Control Framework for Fatigue-Aware Wake Steering in Wind Farms |
| title_full | A Supervisory Control Framework for Fatigue-Aware Wake Steering in Wind Farms |
| title_fullStr | A Supervisory Control Framework for Fatigue-Aware Wake Steering in Wind Farms |
| title_full_unstemmed | A Supervisory Control Framework for Fatigue-Aware Wake Steering in Wind Farms |
| title_short | A Supervisory Control Framework for Fatigue-Aware Wake Steering in Wind Farms |
| title_sort | supervisory control framework for fatigue aware wake steering in wind farms |
| topic | wake steering yaw optimization wind farm supervisory control fatigue-aware control |
| url | https://www.mdpi.com/1996-1073/18/13/3452 |
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