Combined wake control of aligned wind turbines for power optimization based on a 3D wake model considering secondary wake steering

The wake of the wind turbines leads to power losses in wind farms. The active wake control (AWC) method reduces the wake effect and increases the power output. This paper systematically studies the combined wake control (CWC) method, which simultaneously controls the yaw angles, rotor speeds, and pitch angles of turbines to enhance the power output of wind farms. The prediction of the spatial distribution of the wake velocity under different operating states of wind turbines is a key factor in AWC. This study proposes a novel three-dimensional wake model for yawed wind turbine, considering wind shear and secondary steering. This model is validated through comparisons with large eddy simulation. A database for the aerodynamic performances of the NREL 5 MW wind turbine is then established based on OpenFAST. The whale optimization algorithm is used to optimize the wake effects between wind turbines in order to maximize the overall power output of a wind farm consisting of five aligned NREL 5 MW turbines. The obtained results indicate that CWC effectively improves the power output of the wind farm, with better performance than the axial induce factor control and wake redirection control by leveraging more turbine degrees of freedom. Moreover, secondary steering effects allow some downstream turbines need only slight active yaw adjustments to deflect wake considerably. Variations in inflow wind speed, ambient turbulence intensity, and turbine streamwise spacing all influence CWC. Overall, the more serious the wake effects within the wind farm, the greater the power improvement offered by CWC.