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Wake steering, the intentional yaw misalignment of certain turbines in anarray, has demonstrated potential as a wind farm control approach to increasecollective power. Existing algorithms optimize the yaw misalignment angleset-points using steady-state wake models and either deterministic frameworks,or optimizers which account for wind direction and yaw misalignment variabilityand uncertainty. Wake models rely on parameterizations of physical phenomena inthe mean flow field, such as the wake spreading rate. The wake model parametersare uncertain and vary in time at a wind farm depending on the atmosphericconditions, including turbulence intensity, stability, shear, veer, and otheratmospheric features. In this study, we develop a yaw set-point optimizationapproach which includes model parameter uncertainty, in addition to windcondition variability and uncertainty. The optimization is tested in open-loopcontrol numerical experiments using utility-scale wind farm operational datafor which the set-point optimization framework with parameter uncertainty has astatistically significant impact on the wind farm power production for certainwind turbine layouts at low turbulence intensity, but the results are notsignificant for all layouts considered nor at higher turbulence intensity. Theset-point optimizer is also tested for closed-loop wake steering control of amodel wind farm in large eddy simulations of a convective atmospheric boundarylayer. The yaw set-point optimization with model parameter uncertainty improvedthe robustness of the closed-loop wake steering control to increases in the yawcontroller update frequency. Increases in wind farm power production were notstatistically significant due to the high ambient power variability in theturbulent, convective ABL.

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