Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification

WTs are in yaw during most of the operating time. The wake effect of the yawed Wind turbine (WT) leads to lower wind speed and increased turbulence in the wake region of the WT, which leads to increased dynamic load and lower output power of the downstream WT and limits the safety and economic efficiency of the wind farm to a certain extent. Inspired by the 3DJGF model and Jiménez model, this paper proposes a Yawed-3D Jensen-Gaussian full wake (Y-3DJGF) model considering wind shear and double Gaussian distribution over the wake region which is computationally inexpensive and efficient with the 3D wake distribution characteristics of the yaw WT can be obtained. Moreover, a Doppler Light Detection and Ranging (LiDAR)-based field experimental is conducted in a wind farm in North China, and the SCADA data are integrated to analyze the wake data under yaw conditions and verify the accuracy of the Y-3DJGF model with the verification of the proposed model. The results showed that the average of the relative error between the Y-3DJGF model and the measured data was 4.974%, and the fit with the measured data was better than other 5 typical models. Works in this paper can provide reference for improving the energy output of WT and dynamic load analysis of downstream WT.