A Supervisory Control Framework for Fatigue-Aware Wake Steering in Wind Farms

Yang Shen (), Jinkui Zhu, Peng Hou, Shuowang Zhang, Xinglin Wang, Guodong He, Chao Lu, Enyu Wang and Yiwen Wu
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Yang Shen: Zhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, China
Jinkui Zhu: Zhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, China
Peng Hou: Zhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, China
Shuowang Zhang: State Key Laboratory of Offshore Wind Power Equipment and Wind Energy High-Efficient Utilization, Xiangtan 411102, China
Xinglin Wang: State Key Laboratory of Offshore Wind Power Equipment and Wind Energy High-Efficient Utilization, Xiangtan 411102, China
Guodong He: Zhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, China
Chao Lu: Zhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, China
Enyu Wang: Zhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, China
Yiwen Wu: Zhejiang Baima Lake Laboratory Co., Ltd., Hangzhou 310051, China

Energies, 2025, vol. 18, issue 13, 1-16

Abstract: 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.

Keywords: wake steering; yaw optimization; wind farm supervisory control; fatigue-aware control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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