Abstract
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.
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Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Supported by the Key R&D Program of Shandong Province, China (No. 2023ZLYS01), the National Key R&D Program of China (No. 2022YFC3104200), the National Natural Science Foundation of China (No. 12302301), the China Postdoctoral Science Foundation (No. 2023M742229), and the Zhejiang Provincial Natural Science Foundation (ZJNSF) (No. LQ22F030002)
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Li, Y., Gao, Z., Li, S. et al. A nonlinear wake model of a wind turbine considering the yaw wake steering. J. Ocean. Limnol. (2023). https://doi.org/10.1007/s00343-023-3040-6
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DOI: https://doi.org/10.1007/s00343-023-3040-6