A comprehensive understanding of the wake development of wind turbines is essential for improving the power yield of wind farms and for reducing the structural loading of the turbines.Reducing the overall negative impact of wake flows on individual turbines in a farm is one goal of wind farm control. We aim to demonstrate the applicability of yaw control for deflecting wind turbine wakes in a full-scale field experiment. For this purpose, we conducted a measurement campaign at a multimegawatt onshore wind turbine including inflow and wake flow measurements using ground-and nacelle-based long-range light detection and ranging devices. Yaw misalignments of the turbine with respect to the inflow direction of up to 20 • were investigated. We were able to show that under neutral atmospheric conditions, these turbine misalignments cause lateral deflections of its wake. Larger yaw misalignments resulted in greater wake deflection.Because of the inherent struggle in capturing complex and highly dynamic ambient conditions in the field using a limited number of sensors, we particularly focused on providing a comprehensive and comprehensible description of the measurement setup, including the identification of potential uncertainties. KEYWORDSatmospheric boundary layer, atmospheric inflow, lidar, wake deflection, wind farm control INTRODUCTIONWind turbines in a wind farm are typically subjected to mutual aerodynamic interactions due to their wakes. Depending on the farm layout and inflow direction, this can lead to unfavourable structural loading and a substantial reduction in the power yield over extended periods of time . 1-3 Reducing the overall negative impact of wake flows on individual turbines in a farm is one goal of wind farm control. However, a successful implementation of control mechanisms requires a broad understanding of the flow development for various ambient conditions and the interaction between turbines and the flow.One specific approach that has proven its potential in simulations and wind tunnel experiments is wake deflection through turbine operation under yaw misalignment. In this case, an offset between the inflow direction and the orientation of a turbine is deliberately introduced to alter its wake trajectory. Hereinafter, we refer to this method as yaw control for the sake of simplicity. It aims to generate more favourable inflow conditions for downstream turbines by reducing the wake effects. This can, for example, be used to maximize the power output of a wind farm, to mitigate power fluctuation or to reduce turbine loads. With respect to power maximization, it should be considered that misaligned wind turbines generate less power. Therefore, it must be ensured that the power increase generated by the downstream turbines is sufficient for improving the overall power yield of the wind farm.The concept of wake deflection has already been successfully applied in wind tunnel experiments by Clayton and Filby in 1982. 4 Further investigations of the power yield and characteristics of the downstream d...

Abstract. Our aim with this paper was the analysis of the influence of offshore cluster wakes on the power of a far-distant wind farm. We measured cluster wakes with long-range Doppler light detection and ranging (lidar) and satellite synthetic aperture radar (SAR) in different atmospheric stabilities and analysed their impact on the 400 MW offshore wind farm Global Tech I in the German North Sea using supervisory control and data acquisition (SCADA) power data. Our results showed clear wind speed deficits that can be related to the wakes of wind farm clusters up to 55 km upstream in stable and weakly unstable stratified boundary layers resulting in a clear reduction in power production. We discussed the influence of cluster wakes on the power production of a far-distant wind farm, cluster wake characteristics and methods for cluster wake monitoring. In conclusion, we proved the existence of wake shadowing effects with resulting power losses up to 55 km downstream and encouraged further investigations on far-reaching wake shadowing effects for optimized areal planning and reduced uncertainties in offshore wind power resource assessment.

Abstract. The objective of this paper was the experimental investigation of the accumulated induction effect of a large offshore wind farm as a whole, i.e. the global-blockage effect, in relation to atmospheric-stability estimates and wind farm operational states. We measured the inflow of a 400 MW offshore wind farm in the German North Sea with a scanning long-range Doppler wind lidar. A methodology to reduce the statistical variability of different lidar scans at comparable measurement conditions was introduced, and an extensive uncertainty assessment of the averaged wind fields was performed to be able to identify the global-blockage effect, which is small compared to e.g. wind turbine wake effects and ambient variations in the inflow. Our results showed a 4 % decrease in wind speed (accuracy range of 2 % to 6 %) at transition piece height (24.6 m) upwind of the wind farm with the turbines operating at high thrust coefficients above 0.8 in a stably stratified atmosphere, which we interpreted as global blockage. In contrast, at unstable stratification and similar operating conditions and for situations with low thrust coefficients (i.e. approx. 0 for not operating turbines and ≤ 0.3 for turbines operating far above rated wind speed) we identified no wind speed deficit. We discussed the significance of our measurements and possible sources of error in long-range scanning lidar campaigns and give recommendations on how to measure small flow effects like global blockage with scanning Doppler lidar. In conclusion, we provide strong evidence for the existence of global blockage in large offshore wind farms in stable stratification and the turbines operating at a high thrust coefficient by planar lidar wind field measurements. We further conclude that global blockage is dependent on atmospheric stratification.

Abstract. The prospects of active wake deflection control to mitigate wake-induced power losses in wind farms have been demonstrated by large eddy simulations, wind tunnel experiments, and recent field tests. However, it has not yet been fully understood how the yaw control of wind farms should take into account the variability in current environmental conditions in the field and the uncertainty in their measurements. This research investigated the influence of dynamic wind direction changes on active wake deflection by intended yaw misalignment. For this purpose the wake model FLORIS was used together with wind direction measurements recorded at an onshore meteorological mast in flat terrain. The analysis showed that active wake deflection has a high sensitivity towards short-term wind directional changes. This can lead to an increased yaw activity of the turbines. Fluctuations and uncertainties can cause the attempt to increase the power output to fail. Therefore a methodology to optimize the yaw control algorithm for active wake deflection was introduced, which considers dynamic wind direction changes and inaccuracies in the determination of the wind direction. The evaluation based on real wind direction time series confirmed that the robust control algorithm can be tailored to specific meteorological and wind farm conditions and that it can indeed achieve an overall power increase in realistic inflow conditions. Furthermore recommendations for the implementation are given which could combine the robust behaviour with reduced yaw activity.

Abstract. The prospects of active wake deflection control to mitigate wake-induced power losses in wind farms have been demonstrated by large eddy simulations, wind tunnel experiments and recent field tests. However, it has not yet been fully understood how the yaw control of wind farms should take into account the variability of current environmental conditions in the field and the uncertainty of their measurements. This research investigated the influence of dynamic wind direction changes on active wake deflection by intended yaw misalignment. For this purpose the wake model FLORIS was used together with 5 wind direction measurements recorded at an onshore met mast in flat terrain. The analysis showed that active wake deflection has a high sensitivity towards short-term wind directional changes. This can lead to an increased yaw activity of the turbines.Fluctuations and uncertainties can cause the attempt to increase the power output to fail. Therefore a methodology to optimise the yaw control algorithm for active wake deflection was introduced, which considers dynamic wind direction changes and inaccuracies in the determination of the wind direction. The evaluation based on real wind direction time series confirmed that 10 the robust control algorithm can be tailored to specific meteorological and wind farm conditions and that it can indeed achieve an overall power increase in realistic inflow conditions. Furthermore recommendations for the implementation are given which could combine the robust behaviour with reduced yaw activity.