Michael Howland - Wind farm wake steering control under transient atmospheric conditions

Historically, control protocols have optimized the performance of individual wind turbines resulting in aerodynamic wakes which typically reduce total wind farm power production 10-20%. Wake steering, the intentional yaw misalignment of turbines in a wind farm to deflect energy deficit wake regions, has demonstrated potential as a wind farm control approach to increase collective power production. Leveraging aerodynamic wind farm models, we designed a physics-based, data-assisted wake steering control method to increase the power production of wind farms. Parameters in aerodynamic wake models are inherently uncertain. We develop approaches for the efficient calibration and uncertainty quantification of wake model parameters and we perform optimization under model parameter uncertainty. The method was tested in a multi-turbine array at a utility-scale wind farm, where it statistically significantly increased the power production over standard operation. The analytical gradient-based power optimization methodology we developed can optimize the yaw misalignment angles for large wind farms on the order of seconds, enabling online real-time control. To improve wake steering control in transient ABL conditions, we developed a closed-loop wake steering control strategy, which is tested in large-eddy simulations of the terrestrial diurnal cycle, altogether, the results indicate that closed-loop wake steering control can significantly increase wind farm power production over greedy operation provided that site-specific wind farm data is assimilated into the aerodynamic model.