Wake steering under inflow wind direction uncertainty: an LES study

Abstract. Wake steering through static yaw control is a promising wind farm flow control strategy, however full scale implementation in real wind farms is hampered by uncertainties not typically present in a simulation environment. The most notable of these are bias and variability in inflow wind direction - which are both inherent in the atmosphere and introduced through imperfect measurements. To investigate the impact of these uncertainties, LES is conducted on a row of four turbines in a conventionally neutral boundary layer, using three yaw configurations (an unyawed baseline, the leading turbine yawed and the first three turbines yawed) under different inflow wind directions ∈ [−5, 8]°. The impact of the applied yaw strategy and mean wind direction offset is first studied, considering the asymmetry introduced by veer, mean wake shape, changes in local inflow angle and individual turbine power and loads. Considering mean wind farm power output, the inflow wind direction standard deviation in the current study (σ_WD = 2.3°) results in a beneficial window for wake steering of 8.5° (∈ [−1.5, 7.0]°) with peak total power gains of 23 % and 7.5 % for the two yaw strategies, respectively. Extrapolating to an uncertainty of σ_WD = 4.5° using a Gaussian convolution reduces the beneficial ranges to 8° and 6.5° respectively, with peak gains of 7.5 % and 2 %. While exact numbers depend on turbine spacing, the substantial decrease in peak power and narrow range of power gains signify that wake steering is highly sensitive to wind direction uncertainty and small biases in mean inflow wind direction. Therefore, accurate measurement of these quantities and inclusion of them in prediction models is essential.