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A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control

Citations

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Cited by:

  1. Moss, Coleman & Maulik, Romit & Iungo, Giacomo Valerio, 2024. "Augmenting insights from wind turbine data through data-driven approaches," Applied Energy, Elsevier, vol. 376(PA).
  2. Tian, Runze & Kou, Peng & Zhang, Yuanhang & Mei, Mingyang & Zhang, Zhihao & Liang, Deliang, 2024. "Residual-connected physics-informed neural network for anti-noise wind field reconstruction," Applied Energy, Elsevier, vol. 357(C).
  3. He, Ruiyang & Yang, Hongxing & Lu, Lin & Gao, Xiaoxia, 2024. "Site-specific wake steering strategy for combined power enhancement and fatigue mitigation within wind farms," Renewable Energy, Elsevier, vol. 225(C).
  4. Zongxu Liu & Hui Guo & Yingshuai Zhang & Zongliang Zuo, 2025. "A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges," Energies, MDPI, vol. 18(2), pages 1-17, January.
  5. He, Ruiyang & Deng, Xiaowei & Li, Yichun & Dong, Zhikun & Gao, Xiaoxia & Lu, Lin & Zhou, Yue & Wu, Jianzhong & Yang, Hongxing, 2023. "Three-dimensional yaw wake model development with validations from wind tunnel experiments," Energy, Elsevier, vol. 282(C).
  6. Liu, Songyang & Xin, Zhiqiang & Wang, Lei & Xu, Yanming & Cai, Zhiming, 2025. "Fluid–structure interaction simulation of the effect of static yaw control on the aerodynamic responses and wake characteristics of floating offshore wind turbines," Energy, Elsevier, vol. 330(C).
  7. Chen, Yuejiang & Xiao, Jiang-Wen & Wang, Yan-Wu & Luo, Yunfeng, 2025. "Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation," Applied Energy, Elsevier, vol. 377(PA).
  8. Yang, Han & Yuan, Weimin & Zhu, Weijun & Sun, Zhenye & Zhang, Yanru & Zhou, Yingjie, 2024. "Wind turbine airfoil noise prediction using dedicated airfoil database and deep learning technology," Applied Energy, Elsevier, vol. 364(C).
  9. Wang, Yize & Liu, Zhenqing & Hu, Yilu & Bai, Guangpu, 2026. "A coherent power-load optimization algorithm for wind farm-level yaw control considering wake effects via deep neural network," Renewable Energy, Elsevier, vol. 257(C).
  10. Boudy Bilal & Kaan Yetilmezsoy & Mohammed Ouassaid, 2024. "Benchmarking of Various Flexible Soft-Computing Strategies for the Accurate Estimation of Wind Turbine Output Power," Energies, MDPI, vol. 17(3), pages 1-36, February.
  11. Yi, Jun & Qi, ZhongLi & Li, XiangChengZhen & Liu, Hong & Zhou, Wei, 2024. "Spatial correlation-based machine learning framework for evaluating shale gas production potential: A case study in southern Sichuan Basin, China," Applied Energy, Elsevier, vol. 357(C).
  12. Kim, Taewan & Kim, Changwook & Song, Jeonghwan & You, Donghyun, 2024. "Optimal control of a wind farm in time-varying wind using deep reinforcement learning," Energy, Elsevier, vol. 303(C).
  13. Dong, Fuxiang & Wang, Zhonghao & Mu, Chunjin & Liu, Jinfu & Yu, Daren & Li, Hong, 2025. "Wide angle range wind direction ultra-short-term interval prediction based on an improved loss function," Energy, Elsevier, vol. 334(C).
  14. He, Ruiyang & Yang, Hongxing & Lu, Lin, 2023. "Optimal yaw strategy and fatigue analysis of wind turbines under the combined effects of wake and yaw control," Applied Energy, Elsevier, vol. 337(C).
  15. Wang, Yize & Liu, Zhenqing, 2025. "Fatigue analysis of wind turbine and load reduction through wind-farm-level yaw control," Energy, Elsevier, vol. 326(C).
  16. Tiago R. Lucas Frutuoso & Rui Castro & Ricardo B. Santos Pereira & Alexandra Moutinho, 2025. "Advancements in Wind Farm Control: Modelling and Multi-Objective Optimization Through Yaw-Based Wake Steering," Energies, MDPI, vol. 18(9), pages 1-29, April.
  17. Wang, Wenwen & Kong, Xiaobing & Li, Gangqiang & Liu, Xiangjie & Ma, Lele & Liu, Wenting & Lee, Kwang Y., 2024. "Wind farm control using distributed economic MPC scheme under the influence of wake effect," Energy, Elsevier, vol. 309(C).
  18. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
  19. Cheng, Biyi & Yao, Yingxue & Qu, Xiaobin & Zhou, Zhiming & Wei, Jionghui & Liang, Ertang & Zhang, Chengcheng & Kang, Hanwen & Wang, Hongjun, 2024. "Multi-objective parameter optimization of large-scale offshore wind Turbine's tower based on data-driven model with deep learning and machine learning methods," Energy, Elsevier, vol. 305(C).
  20. Tu, Yu & Chen, Yaoran & Zhang, Kai & He, Ruiyang & Han, Zhaolong & Zhou, Dai, 2025. "A multi-fidelity framework for power prediction of wind farm under yaw misalignment," Applied Energy, Elsevier, vol. 377(PC).
  21. Luo, Zhaohui & Wang, Longyan & Xu, Jian & Wang, Zilu & Yuan, Jianping & Tan, Andy C.C., 2024. "A reduced order modeling-based machine learning approach for wind turbine wake flow estimation from sparse sensor measurements," Energy, Elsevier, vol. 294(C).
  22. Sun, Bingchuan & Ooi, Kim Tiow & Su, Mingxu, 2026. "Wind turbine blade damage: A systematic review of detection, diagnosis, performance impact, and lifecycle health management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 230(C).
  23. Wang, Qiulei & Hu, Junjie & Yang, Shanghui & Ti, Zilong & Deng, Xiaowei, 2026. "Knowledge-Fusion Graph Transformer network for wind farm assessment with sparse data," Renewable Energy, Elsevier, vol. 256(PI).
  24. Cristofer Aguilar Jiménez & Geovanni Hernández Gálvez & José Rafael Dorrego Portela & Antonio Verde Añorve & Guillermo Ibáñez Duharte & Joel Pantoja Enríquez & Orlando Lastres Danguillecourt & Alberto, 2025. "Sustainable Analysis of Wind Turbine Blade Fatigue: Simplified Method for Dynamic Load Measurement and Life Estimation," Sustainability, MDPI, vol. 17(17), pages 1-30, August.
  25. Zhu, Xiaoxun & Chen, Yao & Xu, Shinai & Zhang, Shaohai & Gao, Xiaoxia & Sun, Haiying & Wang, Yu & Zhao, Fei & Lv, Tiancheng, 2023. "Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification," Energy, Elsevier, vol. 270(C).
  26. Tao, Siyu & Yang, Jisheng & Jiang, Fuqing & Yang, Hongxing & Zheng, Gang & Feijóo-Lorenzo, Andrés E. & He, Ruiyang, 2026. "Active yaw control strategy for a hybrid offshore wind farm under typical wind conditions," Renewable Energy, Elsevier, vol. 259(C).
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