Dynamic control strategies, such as dynamic induction control [1], were reported as efficient and beneficial in improving wind farm power production. A recent work based on computational fluid dynamics simulation [2] shows that by controlling wake meandering through cyclic yaw angle variation, a faster wake recovery and higher available power in the wake of a wind turbine can be achieved. To investigate the potential of this cyclic yaw control (CYC) strategy in wind farm power improvement, we conducted wind tunnel experiments on a model wind farm consisting of three miniature wind turbines (arranged to be aligned with the inflow direction with a streamwise spacing of five rotor diameters). The yaw angle of the most upwind turbine was controlled to vary sinusoidally. The two control parameters, including the yaw angle amplitude and the yaw Strouhal number (i.e., the normalized frequency), were adjusted to optimize the power performance of the wind farm.
Based on both power and wake measurements, we found that cyclic yawing can enhance the lateral flow entrainment and thus increase the power production of the wind farm. The power performance of the wind farm is found to be dependent on the control parameters. A maximum power gain of 15.2% is achieved in our study. We found that the controlled wake meandering dynamics are highly periodic. The phase-averaged wake center trajectory also highly resembles the sine wave, making it possible to predict the instantaneous wake deflection using the wave equation. Furthermore, it is found that the amplitude of periodic wake meandering first increases and then decreases with the increase of the downstream distance from the turbine location. The critical downstream distance (where the amplitude attenuation starts) is found to be around one wavelength. At the growth stage, the amplitude can be well predicted with the yawed wake model [3] (at a yaw angle equal to the yaw angle amplitude of CYC), while the physics of amplitude decay still needs to be better understood to predict the decrease stage. The amplitude decrease can be related to the damping effects (i.e., energy dissipation) due to turbulent wake mixing, which will be considered in future work on analytical model development.

Spaceborne Synthetic Aperture Radar (SAR) is a potentially valuable data source for the offshore wind industry. However, the local bathymetry at coastal areas, where bottom-fixed offshore wind farms are installed, complicates the relation between the surface wind speed and the observed backscattering of the sea surface. Surface winds over two shallow water domains are derived from SAR images by the CMOD5.N geophysical model function. The first domain is bounded by a complex coastline and the other contains offshore wind farms. SAR surface winds are compared to measurements and simulations performed with the Weather Research and Forecasting (WRF) code and its assimilation system WRFDA. Extension and characteristics of simulated wind farm wakes and SAR images are also compared.

The European Train2Wind project plans to conduct an experimental campaign at an offshore wind farm in the Baltic Sea in March 2023 .

This campaign will involve the use of unmanned aerial systems (UAS) of type multicopter by a team from the University of Tübingen in order to study the evolution of the lower atmosphere near and above the wind farm. The multicopter will be launched and landed from a vessel located directly adjacent to the turbines and will perform vertical profiles, measuring temperature, humidity, wind direction, wind magnitude, and aerosol particles. The data will allow for the first time in-situ measurements of the height of the internal boundary layer caused by the wind farm and assess how the flow is altered by the presence of turbines.

A previous preparation campaign was conducted in September 2022 in collaboration with the wind farm operator RWE, during which the team was able to operate a multicopter from a maintenance vessel.
Operating a UAS from a boat presents many challenges, however, vertical profiles were conducted up to 120 m and have shown that this type of measurement method can effectively capture the development of a wind turbine wake.

The results from the UAS measurements of the two campaigns will be presented. In addition to this, UAS data will be compared with the other measurement approaches (satellite imaging, remote sensing, simulations) conducted by the Train2Wind consortium at the exact location during this campaign. This will create a unique dataset for the future investigation of the entrainment of large offshore wind parks.