In recent years, the relevance of the interaction between neighboring wind farms has grown steadily. As one farm extracts energy from the wind, a downstream one can systematically experience lower wind speeds which threatens the economic viability of the farm. Significant progress has been made in understanding these farm-farm wake interactions, but we still lack methodologies to mitigate their undesired effects. In this study, we introduce Active Cluster Wake Mixing (ACWM). This novel method aims to accelerate the recovery of the cluster wake using dynamic control actions: By exciting the thrust of the individual turbines depending on their relative location, we generate non-uniform patterns of energy extraction. Phase offsets between the individual excitation signals propagate these regions through the wind farm. This results in large-scale velocity gradients inside the farm, which also affect the flow in the cluster wake region. An in-depth exploration and optimization of ACWM requires significant computational effort. Therefore, we compare three different wind farm modeling approaches in Large Eddy Simulations (LES) that differ in their computational costs regarding their suitability for further exploration of ACWM. For this purpose, we use an unoptimized ACWM scheme with two different excitation frequencies. For the first time ever we successfully show that ACWM manipulates the flow inside the wind farm with favorable effects on the wake velocity. We also demonstrate that the modeling of cluster wakes is challenging and has a significant effect on the potential gain.

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A variety of wind farm control strategies exist in order to reduce unfavorable wake effects in large wind farms. While strategies like wake steering already reached a high maturity level, it is interesting to compare them to more recently proposed strategies. Such a comparison can form the basis for the development of a symbiotic wind farm control toolbox, from which a control strategy is chosen and activated depending on the operating conditions. The present study compares wake steering with helix control across a wide range of turbine spacings and wind directions using large-eddy simulation (LES). The size of the search space is made computationally tractable for LES by adopting a setup based on one physical upstream turbine and a distribution of virtual downstream turbines which do not exert any thrust force. It is found that helix control is beneficial for full wake overlap and turbine spacing of less than six rotor diameters whereas wake steering proves to be optimal further downstream and for partial wake overlap. Furthermore, the results show that the helix control setpoint in the proximity of full wake overlap scenarios is less susceptible to wind direction variations. This finding indicates that the combination of wake steering and helix control has potential for the design of a wind farm controller which is more robust in full wake overlap scenarios and can reduce the need for large yaw offset adjustments.

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The performance of wind farms can substantially increase when their individual turbines deviate from their own greedy control strategy and instead also take into account downstream turbines operating in the wake. The helix approach is a recently introduced dynamic wind farm control strategy that tackles this issue by leveraging individual pitch control to accelerate wake recovery. Its effective implementation requires detailed knowledge about the scaling between control input and the resulting power gain and turbine loading across the farm. In the present work this scaling is explored by means of large-eddy simulation of a two-turbine farm in the conventionally neutral atmospheric boundary layer. A parameter sweep for the amplitude of the helix is performed showing monotonous increase of the farm's power output with increasing pitch amplitude within the considered range of zero to six degrees. The scaling of the power gain suggests that a threshold amplitude should be exceeded for effective speed-up of the wake recovery, whereas the damage equivalent loads computed for the turbines indicate an upper limit for the amplitude despite increasing power gains.

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In most current offshore wind farms, the turbines are controlled greedily, neglecting any coupling by wake effects with other turbines. By neglecting these effects of aerodynamic interactions, the power production performance is substantially reduced. Besides the well-known wake steering and dynamic induction control wake control strategies, a novel wind farm flow control strategy called the Helix approach has been recently proposed to mitigate the impacts of wake effects and optimize wind farm performance. The Helix approach adopts the individual pitch control (IPC) technique to dynamically deform the wake into the helical shape, which induces wake instability and thereby stimulates wake recovery. The first results employing a single-harmonic signal have demonstrated promising enhancement in wake recovery effects. However, more complex signals to potentially improve the effectiveness of the Helix approach have never been studied. This paper explores the potential of using higher-harmonic signals in the Helix approach to further enhance wake mixing. The aeroelastic simulator, OpenFAST, with its recently developed free vortex wake codes is adopted to simulate the dynamic wake evolution. A Fourier stability analysis is used to quantitatively identify the wake breakdown position. Results show that in the baseline case where no Helix signals are implemented, the wake breaks down at 3.25 rotor diameter (D) from the rotor. When using the designed multi-sine Helix signals, the wake breaks down at 1.75 D from the rotor, which is a significant improvement over the breakdown distance at 2.50 D using the conventional single-sine Helix. The earlier wake breakdown indicates faster wake recovery and is to be validated by future higher-fidelity simulation studies.@en

The development of new wind farm control strategies can benefit from combined analysis of flow dynamics in the farm and the behavior of individual turbines within one simulation environment. In this work, we present such an environment by developing a new coupling between the large-eddy simulation (LES) code GRASP and the multiphysics wind turbine simulation tool OpenFAST via an actuator line model (ALM). In addition, the implementation of the recently proposed filtered actuator line model (FALM) within the coupling is described. The new ALM implementation is cross-verified with results from four other commonly used research LES codes. The results for the blade loads and the near wake obtained with the new coupling are consistent with the other codes. Deviations are observed in the far wake. The results further indicate that the FALM is able to reduce the lift and power overprediction from which the traditional ALM suffers on coarse LES grids. This new simulation environment paves the way for future wind farm simulations under realistic weather conditions by leveraging GRASP's ability to impose data from large-scale meteorological models as boundary conditions.

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