A double-Gaussian wake model considering yaw misalignment - Soesanto, Qidun Maulana Binu; Soesanto, Qidir Maulana Binu; Widiyanto, Puji (International Journal of Renewable Energy Development, 2025)
A wake steering has been known to effectively increase wind farm production by deflecting the upstream turbines’ wakes via yaw misalignment, thus minimizing their negative impacts on the downstream turbines' performances. This study presents analytical modeling of horizontal-axis wind turbine (HAWT) wake using low-cost analytical modeling as an alternative to expensive numerical and experimental trials. The existing double-Gaussian (DG) analytical wake model was modified to include the yaw misalignment effect, allowing its usability for the yawed HAWT wake modeling. The benchmark dataset produced by high-fidelity large eddy simulation (LES) of wake flowfields behind the turbine with yaw angles of 0º, 10º, 20º, and 30º were used to validate the accuracy of the DG yaw wake model. Overall, the DG yaw wake model predictions showed good agreement with the benchmark dataset under varying HAWT rotor yaw configurations. The analytical results verified by the LES dataset confirm the effectiveness of yaw misalignment in deflecting the wake trajectory, expediting the wake recovery downstream of the HAWT. In addition, a higher rotor yaw angle improves the wake recovery rate in the prevailing wind direction. Notable deviations against the benchmark dataset were found mainly within the near-wake region owing to flow acceleration arising from turbine-induced turbulence. As a result, the model’s predictions were slightly lower than the benchmark dataset, most likely due to neglecting the acceleration term in the analytical model derivation. Otherwise, the analytical model could accurately predict the mean wake velocity within the far-wake region for all evaluated cases, demonstrating its reliability in estimating wind speed potential within a practical distance for micrositing. These results were also proved quantitatively by statistical evaluations utilizing root mean square error (RMSE) and Pearson correlation coefficient R. The present study points out the importance of the upstream HAWTs’ rotor yaw controls to properly deflect their wakes away from their mainstream trajectories, thus effectively maximizing the wind speed potentials extracted by the downstream HAWTs and improving the overall wind farm production.