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<p>Compared to fixed-bottom offshore wind farms, offshore floating wind farms experience unstable platform motions that induce rotor misalignment. Such misalignment causes wake redirection, necessitating its consideration in wake modeling for enhanced accuracy. This study focuses on: developing an analytical solution for wake center deflection incorporating rotor misalignment based on counter-rotating vortex pair kinematics; proposing a 3D wake model using this deflection solution for wind speed prediction; and validating the analytical model against high-fidelity simulations with the coupled SOWFA-OpenFAST framework. The results computed by the dynamic wake model exhibit excellent agreement with Large Eddy Simulation (LES) data, confirming its strong capability in predicting spatial wake distributions under rotor misalignment conditions and accurately identifying wake positions. Finally, a dynamic wake model is derived using the Lagrangian approach. This research provides a predictive framework for offshore floating wind farm wakes, enhances wind farm power output forecasting accuracy, and establishes a computational foundation for effective wake steering control.</p>

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Wake modeling for offshore floating wind farms incorporating time-lag effects in yaw and pitch states

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月27日

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  • Liye Zhao,
  • Feiyang Qiao,
  • Qianqian Li,
  • Xiangyang Mu,
  • Yingyue Pan,
  • Yongxiang Gong

摘要

Compared to fixed-bottom offshore wind farms, offshore floating wind farms experience unstable platform motions that induce rotor misalignment. Such misalignment causes wake redirection, necessitating its consideration in wake modeling for enhanced accuracy. This study focuses on: developing an analytical solution for wake center deflection incorporating rotor misalignment based on counter-rotating vortex pair kinematics; proposing a 3D wake model using this deflection solution for wind speed prediction; and validating the analytical model against high-fidelity simulations with the coupled SOWFA-OpenFAST framework. The results computed by the dynamic wake model exhibit excellent agreement with Large Eddy Simulation (LES) data, confirming its strong capability in predicting spatial wake distributions under rotor misalignment conditions and accurately identifying wake positions. Finally, a dynamic wake model is derived using the Lagrangian approach. This research provides a predictive framework for offshore floating wind farm wakes, enhances wind farm power output forecasting accuracy, and establishes a computational foundation for effective wake steering control.