A nonlinear wake model of a wind turbine considering the yaw wake steering

被引:0
|
作者
Yunzhou LI [1 ,2 ,3 ]
Zhiteng GAO [4 ]
Shoutu LI [2 ]
Suiping QI [5 ]
Xiaoyu TANG [6 ]
机构
[1] College of Meteorology and Oceanography,National University of Defense Technology
[2] School of Energy and Power Engineering,Lanzhou University of Technology
[3] Laoshan Laboratory
[4] Multi-function Towing Tank,School of Naval Architecture,Ocean and Civil Engineering,Shanghai Jiao Tong University
[5] Institute of Oceanographic Instrumentation,Qilu University of Technology (Shandong Academy of Sciences)
[6] State Key Laboratory of Industrial Control Technology,College of Control Science and Engineering,Zhejiang University
基金
中国博士后科学基金; 中国国家自然科学基金; 国家重点研发计划;
关键词
D O I
暂无
中图分类号
TM315 [风力发电机];
学科分类号
080801 ;
摘要
Duo to fluctuations in atmospheric turbulence and yaw control strategies, wind turbines are often in a yaw state. To predict the far wake velocity field of wind turbines quickly and accurately, a wake velocity model was derived based on the method of momentum conservation considering the wake steering of the wind turbine under yaw conditions. To consider the shear effect of the vertical incoming wind direction, a two-dimensional Gaussian distribution function was introduced to model the velocity loss at different axial positions in the far wake region based on the assumption of nonlinear wake expansion. This work also developed a “prediction-correction” method to solve the wake velocity field,and the accuracy of the model results was verified in wake experiments on the Garrad Hassan wind turbine. Moreover, a 33-kW two-blade horizontal axis wind turbine was simulated using this method, and the results were compared with the classical wake model under the same parameters and the computational fluid dynamics (CFD) simulation results. The results show that the nonlinear wake model well reflected the influence of incoming flow shear and yaw wake steering in the wake velocity field.Finally, computation of the wake flow for the Horns Rev offshore wind farm with 80 wind turbines showed an error within 8% compared to the experimental values. The established wake model is less computationally intensive than other methods, has a faster calculation speed, and can be used for engineering calculations of the wake velocity in the far wakefield of wind turbines.
引用
收藏
页码:715 / 727
页数:13
相关论文
共 50 条
  • [1] Erratum to: A nonlinear wake model of a wind turbine considering the yaw wake steering
    Li, Yunzhou
    Gao, Zhiteng
    Li, Shoutu
    Qi, Suiping
    Tang, Xiaoyu
    JOURNAL OF OCEANOLOGY AND LIMNOLOGY, 2024, 42 (03) : 1016 - 1016
  • [2] Experimental study on the characteristics of wind turbine wake field considering yaw conditions
    Zhao, Xiuyong
    Hu, Tianyu
    Zhang, Lidong
    Liu, Zhitan
    Wang, Sheng
    Tian, Wenxin
    Yang, Zhile
    Guo, Yuanjun
    ENERGY SCIENCE & ENGINEERING, 2021, 9 (12) : 2333 - 2341
  • [3] A Nonlinear Wind Turbine Wake Expansion Model Considering Atmospheric Stability and Ground Effects
    Han, Xingxing
    Wang, Tongguang
    Ma, Xiandong
    Xu, Chang
    Fu, Shifeng
    Zhang, Jinmeng
    Xue, Feifei
    Cheng, Zhe
    ENERGIES, 2024, 17 (17)
  • [4] Experimental analysis of the wake behind a small wind-turbine model in yaw
    Micheletto, D.
    Segalini, A.
    Fransson, J. H. M.
    WAKE CONFERENCE 2023, 2023, 2505
  • [5] Machine learning to rapidly predict turbine yaw angles for wake steering
    Stanley, Andrew P. J.
    Mulder, Tim
    Doekemeijer, Bart
    Kreeft, Jasper
    SCIENCE OF MAKING TORQUE FROM WIND, TORQUE 2024, 2024, 2767
  • [6] A hybrid wake method for simulating yaw tandem wind turbine
    Yuan, Yuming
    Zhou, Binzhen
    Yang, Zhiwei
    Liu, Bo
    Zhou, Zhipeng
    Li, Mingxin
    OCEAN ENGINEERING, 2024, 313
  • [7] Fast yaw optimization for wind plant wake steering using Boolean yaw angles
    Stanley, Andrew P. J.
    Bay, Christopher
    Mudafort, Rafael
    Fleming, Paul
    WIND ENERGY SCIENCE, 2022, 7 (02) : 741 - 757
  • [8] Wake impacts on downstream wind turbine performance and yaw alignment
    McKay, Phillip
    Carriveau, Rupp
    Ting, David S-K.
    WIND ENERGY, 2013, 16 (02) : 221 - 234
  • [9] Wake Steering for Wind Turbine Fatigue Load Optimisation
    Navalkar, Sachin T.
    Dell, Timothy
    Burillo, Nieves
    2023 AMERICAN CONTROL CONFERENCE, ACC, 2023, : 1499 - 1504
  • [10] Wind turbine wake position detection and rotor speed-based wake steering validation in a wind tunnel wake simulator
    Castillo, Ricardo
    Bayne, Stephen
    Pol, Suhas
    Westergaard, Carsten
    WIND ENGINEERING, 2020, 44 (05) : 483 - 493