Wind tunnel testing of wake control strategies

Campagnolo, Filippo; Petrović, Vlaho; Bottasso, Carlo L.; Croce, Alessandro

doi:10.1109/acc.2016.7524965

Cited by 110 publications

(107 citation statements)

References 7 publications

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“…Wind tunnel tests have been conducted that show encouraging results that match simulation results based on wake redirection (Campagnolo et al (2016); Schottler et al (2016)). In addition, there are preliminary results of the benefits of wake steering from an offshore commercial wind farm (Fleming et al (2017c)).…”

Section: Introductionmentioning

confidence: 61%

Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

Annoni¹,

Fleming²,

Scholbrock³

et al. 2018

Preprint

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Abstract.Wind turbines in a wind farm operate individually to maximize their own performance regardless of the impact of aerodynamic interactions on neighboring turbines. Wind farm controls can be used to increase power production or reduce overall structural loads by properly coordinating turbines. One wind farm control strategy that is addressed in literature is known as wake steering, wherein upstream turbines operate in yaw misaligned conditions to redirect their wakes away from downstream 5 turbines. The National Renewable Energy Laboratory (NREL) in Golden, CO conducted a demonstration of wake steering on a single utility-scale turbine. In this campaign, the turbine was operated at various yaw misalignment setpoints while a lidar mounted on the nacelle scanned five downstream distances. The lidar measurements were combined with turbine data, as well as measurements of the inflow made by a highly instrumented meteorological mast upstream. The full-scale measurements are used to validate controls-oriented tools, including wind turbine wake models, used for wind farm controls and optimization. 10This paper presents a quantitative comparison of the lidar data and controls-oriented wake models under different atmospheric conditions and turbine operation. The results show good agreement between the lidar data and the models under these different conditions.

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Section: Introductionmentioning

confidence: 61%

Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

Annoni¹,

Fleming²,

Scholbrock³

et al. 2018

Preprint

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“…However, simple static induction control failed to increase wind-farm power, indicating the need for more involved induction control strategies. In addition, recent wind tunnel studies have indicated discrepancies between power gains predicted by engineering models and those observed in the wind tunnel, and have reported no significant power gains beyond measurement uncertainties for static axial induction control [9,10]. Furthermore, it is important to note that most of the studies on wind-farm control are performed in worst-case conditions, i.e., in which neighboring turbines are aligned with the mean wind direction, with maximal wake interaction and potential for coordinated control.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization

2018

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Abstract:In wind farms, wakes originating from upstream turbines cause reduced energy extraction and increased loading variability in downstream rows. The prospect of mitigating these detrimental effects through coordinated controllers at the wind-farm level has fueled a multitude of research efforts in wind-farm control. The main strategies in wind-farm control are to influence the velocity deficits in the wake by deviating from locally optimal axial induction setpoints on the one hand, and steering wakes away from downstream rows through yaw misalignment on the other hand. The current work investigates dynamic induction and yaw control of individual turbines for wind-farm power maximization in large-eddy simulations. To this end, receding-horizon optimal control techniques combined with continuous adjoint gradient evaluations are used. We study a 4 × 4 aligned wind farm, and find that for this farm layout yaw control is more effective than induction control, both for uniform and turbulent inflow conditions. Analysis of optimal yaw controls leads to the definition of two simplified yaw control strategies, in which wake meandering and wake redirection are exploited respectively. Furthermore it is found that dynamic yawing provides significant benefits over static yaw control in turbulent flow environments, whereas this is not the case for uniform inflow. Finally, the potential of combining overinductive axial induction control with yaw control is shown, with power gains that approximate the sum of those achieved by each control strategy separately.

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“…In contrast, control strategies at the farm level allow the wake interaction to be influenced and promise to improve overall wind-farm performance by improving wind conditions for downstream turbines. This can be achieved by redirecting propagating wakes (yaw control; see, e.g., Fleming et al, 2014;Gebraad et al, 2016;Campagnolo et al, 2016) or by affecting the induced wake velocity deficits (axial induction control; see, e.g., Nilsson et al, 2015;Annoni et al, 2016;Bartl and Saetran, 2016). A more exhaustive survey of wind-farm control in a broader context can be found in Knudsen et al (2015) and Boersma et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Towards practical dynamic induction control of wind farms: analysis of optimally controlled wind-farm boundary layers and sinusoidal induction control of first-row turbines

Munters

Meyers

2018

Wind Energ. Sci.

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Abstract. Wake interactions between wind turbines in wind farms lead to reduced energy extraction in downstream rows. In recent work, optimization and large-eddy simulation were combined with the optimal dynamic induction control of wind farms to study the mitigation of these effects, showing potential power gains of up to 20 % (Munters and Meyers, 2017, Phil. Trans. R. Soc. A, 375, 20160100, https://doi.org/10.1098/rsta.2016.010). However, the computational cost associated with these optimal control simulations impedes the practical implementation of this approach. Furthermore, the resulting control signals optimally react to the specific instantaneous turbulent flow realizations in the simulations so that they cannot be simply used in general. The current work focuses on the detailed analysis of the optimization results of Munters and Meyers, with the aim to identify simplified control strategies that mimic the optimal control results and can be used in practice. The analysis shows that wind-farm controls are optimized in a parabolic manner with little upstream propagation of information. Moreover, turbines can be classified into first-row, intermediate-row, and last-row turbines based on their optimal control dynamics. At the moment, the control mechanisms for intermediate-row turbines remain unclear, but for first-row turbines we find that the optimal controls increase wake mixing through the periodic shedding of vortex rings. This behavior can be mimicked with a simple sinusoidal thrust control strategy for first-row turbines, resulting in robust power gains for turbines in the entrance region of the farm.

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Wind tunnel testing of wake control strategies

Cited by 110 publications

References 7 publications

Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

Analysis of Control-Oriented Wake Modeling Tools Using Lidar Field Results

Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization

Towards practical dynamic induction control of wind farms: analysis of optimally controlled wind-farm boundary layers and sinusoidal induction control of first-row turbines

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