Wind farm yaw control set-point optimization under model parameter uncertainty

Wake steering, the intentional yaw misalignment of certain turbines in an array, has demonstrated potential as a wind farm control approach to increase collective power. Existing algorithms optimize angle set-points using steady-state wake models and either deterministic frameworks or optimizers that account for direction variability uncertainty. rely on parameterizations physical phenomena mean flow field, such spreading rate. The model parameters are uncertain vary time at depending atmospheric conditions, including turbulence intensity, stability, shear, veer, other features. In this study, we develop set-point optimization includes parameter uncertainty addition condition To enable computationally efficient online under uncertainty, simplified, approximate distribution estimation method is used. tested open-loop numerical experiments utility-scale operational data which framework with parametric statistically significant impact power production turbine layouts low but results not all considered nor higher intensity. optimizer also closed-loop steering large eddy simulations convective boundary layer (ABL). reduced sensitivity increases controller update frequency. Increases were due high ambient turbulent, ABL.