Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high-fidelity flow simulations coupled with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. The wind farm control strategy is optimized for both the power output and the loading of the turbines. The optimization performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggests that a power gain of almost 3%±1% can be achieved at close spacing by yawing the upstream turbine more than 15∘. At larger spacing the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction in the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, as well as the implications for wind farm control.

Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high fidelity flow simulations combined with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. Both the power output and the loading of the turbines are included in the optimization of wind farm control strategies. Optimization results performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggest that a power gain of almost 3 % ± 1 % can be achieved at close spacing by yawing the upstream turbine more than 15°. At larger spacing, the power gain the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction of the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, and the implication for wind farm control.

In this work, a new controls-oriented wake model is modified and compared to an analytical Gaussian wake model, high-fidelity simulation data, and experimental wind tunnel campaign. This model, called the curled wake model, captures a wake phenomenon that occurs behind yawed turbines, modeled as a collection of vortices shed from the rotor plane. Through turbine simulations, these vortices are shown to have a significant impact on the prediction of the wake steering’s performance. Overall, the results support the concept of secondary steering, or a yawed turbine’s ability to deflect the wake of a downstream turbine, and suggest that future turbine wake studies and yaw optimizations should include the curled wake phenomenon.

Wind turbines are typically closely spaced in wind farms, and thus operate in the wake of upstream turbines and experience power losses. Currently, one of the techniques to reduce the wake interaction between turbines within a wind farm is to yaw the upstream turbine with regards to the incident wind direction. The objective is to deflect the wake, which can potentially increase the overall power output and the annual energy production of the wind farm. Experimental data can aid the process to thoroughly analyse the wake deflection under different inflow conditions, which is necessary to apply a yaw-based wind farm control model. The aim of the present research is to investigate the possibility and accuracy of the experimental setup to measure the wake characteristics with a high spatial and temporal resolution through the use of a short-range Lidar WindScanner, within the wind tunnel of ForWind-Oldenburg. This technique provides the opportunity to analyse the flow structures at different operational and inflow conditions in a relative fast manner without disturbing the flow. Experiments were conducted using a model wind turbine in a large cross-section wind tunnel. The short-range Lidar WindScanner is used as the primary instrument to map the wind turbine wake at different downstream locations. The flow structures of the wake were measured from 1 D up to 10 D downstream of the turbine rotor. A stable flow within the wind tunnel segment is measured, which is crucial for the analyses of the evolution of the wake. In addition, a high detailed spatial resolution of the wake profile is observed, showing the symmetric and asymmetric behaviour of the wake, for unyawed and yawed conditions, respectively. Furthermore, the calculation of the thrust coefficient from the velocity data show expected behaviour, giving further credibility to the measurement technique.

Abstract. Redirecting the wake from an upstream wind turbine by yawing its rotor can reduce the negative impact of the wake on a downstream turbine. The present research investigated wind turbine wake behaviour for three yaw angles [-30,0,30∘] at different inflow turbulence levels and shear profiles under controlled conditions. Experiments were conducted using a model wind turbine with 0.6 m diameter (D) in a large wind tunnel. A short-range lidar WindScanner was used to map the wake with high spatial and temporal resolution in vertical, cross-stream planes at different downstream locations and in a horizontal plane at hub height. The lidar WindScanner enabled fast measurements at multiple locations in comparison to the standard hot-wire measurements. The flow structures and the energy dissipation rate of the wake were measured from 1 up to 10 D, and for one inflow case up to 16 D, downstream of the turbine rotor. A strong dependency of the wake characteristics on both the yaw angle and the inflow conditions was observed. In addition, the curled wake that develops under yaw misalignment due to the counter-rotating vortex pair was more pronounced with a boundary layer (sheared) inflow condition than for uniform inflow with different turbulence levels. Furthermore, the lidar velocity data and calculated quantities such as the energy dissipation rate compared favourably with hot-wire data from previous experiments with a similar inflow condition and wind turbine model in the same facility, lending credibility to the measurement technique and methodology used here. The results of this measurement campaign provided a deeper understanding of the development of the wake for different yaw angles and inflow conditions which can help improve wake models.