A novel non-Gaussian analytical wake model of yawed wind turbine

Abstract

Yaw-based wake control optimizes wind farm performance, necessitating an accurate analytical wake model for yawed wind turbines. Existing studies predominantly employ Gaussian models to predict yawed wind turbine wakes, which struggle to capture non-Gaussian characteristics induced by yaw-misalignment, such as bimodal distributions and skewness. This research introduces an innovative non-Gaussian wake framework for yawed wind turbines, incorporating momentum and mass conservation, through numerical and analytical studies. The Rotating Actuator Disk Model-Large Eddy Simulation (ADMR-LES) is used to model a yawed wind turbine, and the wake characteristics including wake deflection, asymmetric patterns, and self-similarity are examined. Our findings inform the development of a comprehensive wake framework for yawed wind turbines, addressing three key aspects: wake deflection, velocity deficit patterns, and added turbulence distribution. The proposed model is validated against both wind tunnel experimental data and numerical simulation data, demonstrating higher accuracy than existing wake models, particularly in describing the asymmetry of wake velocity distribution under yawed conditions and the evolution from bimodal to unimodal distribution.