Modelling yawed wind turbine wakes: Extension of a gaussian-based wake model

Abstract

Yaw-based wake steering control is a potential way to improve wind plant overall performance. For its engineering application, it is crucial to accurately predict the turbine wakes under various yawed conditions within a short time. In this work, a two-dimensional analytical model is proposed for far wake modeling under yawed conditions by taking the self-similarity assumption for the streamwise velocity deficit and skewness angle at hub height. The proposed model can be applied to predict the wake center trajectory, streamwise velocity, and transverse velocity in the far-wake region downstream of a yawed turbine. For validation purposes, predictions by the newly proposed model are compared to wind tunnel measurements and large-eddy simulation data. The results show that the proposed model has significantly high accuracy and outperforms other common wake models. More importantly, the equations of the new proposed model are simple, the wake growth rate is the only parameter to be specified, which makes the model easy to be used in practice.