From wake steering to flow control

Fleming, Paul; Annoni, Jennifer; Churchfield, Matthew J.; Martinez, Luis; Gruchalla, Kenny; Lawson, Michael; Moriarty, Patrick

doi:10.5194/wes-2017-52

Cited by 5 publications

(10 citation statements)

References 9 publications

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“…In particular, positive yaw angles are more effective than negative yaw angles Fleming et al (2017a). Previously, it was speculated that there was a rotation-induced lateral offset that is caused by the interaction of the wake rotation with the shear layer ).…”

Section: Wake Asymmetrymentioning

confidence: 99%

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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: Wake Asymmetrymentioning

confidence: 99%

“…5 Fleming et al (2017a) proposes that there is an asymmetry in the wake that can be described by counter-rotating vortices, turbine rotation, and shear rather than actual deflection. Updates to the FLORIS wake modeling framework to reflect the asymmetry will be done in future work.…”

Section: Wake Asymmetrymentioning

confidence: 99%

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

Annoni¹,

Fleming²,

Scholbrock³

et al. 2018

Preprint

Self Cite

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“…Unlike, the yaw steering investigations performed by e.g. Gebraad et al [8] and Fleming et al [9], the turbine(s) are not intentionally yawed here. The turbines are aligned with the main wind direction, but as shown in Andersen et al [27] the instantaneous wind direction varies significantly (more than ±15 • ) even within an aligned row of turbines.…”

Section: Wind Turbine and Wake Positionmentioning

confidence: 66%

“…Gebraad et al [8] and Fleming et al [9] The aforementioned studies have generally focussed on the changes to the flow, often from steady state or at least static operating conditions. Dynamic control of large wind farms has recently been investigated in detail by Goit and Meyers [10] and subsequently a derived simpler and practical control strategy by Munters and Meyers [11], although the dynamic analysis does not include the effect on the turbine load.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous Response and Mutual Interaction between Wind Turbine and Flow

Andersen¹,

Sørensen²

2018

J. Phys.: Conf. Ser.

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Abstract. The mutual fluid-structure interaction between wind turbine(s) and the highly turbulent flow deep inside a large wind farm is investigated in order to elucidate on how to implement and perform dynamic wind farm control. The study employs a fully coupled LES and aeroelastic framework, which provide time resolved flow and turbine response governed by a controller. The results show a large correlation between incoming flow and turbine response, which extends several radii upstream and could be utilized for turbine control by e.g. installing a lidar on top of the wind turbine. Similarly, the results are valuable for utilizing nacelle mounted lidars for power curve assessments in large wind farms. However, the correlations between turbine and wake flow as well as the dynamic wake position are low, which is potentially discouraging for attempts to do instantaneous yaw steering.

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“…This test case investigates to which degree a partial wake impact can deflect the wake behind a non-yawed downstream turbine. This has recently been investigated in a LES study by Fleming et al (2017). In test case 3, similar to test case 1, the 15 flow 3D and 6D (D = D ForWind ) behind the ForWind turbine is investigated.…”

Section: Test Cases Descriptionmentioning

confidence: 99%

Blind test comparison on the wake behind a yawed wind turbine

Mühle¹,

Schottler²,

Bartl³

et al. 2018

Preprint

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This article summarizes the results of a fifth Blind test workshop, which was held in Visby, Sweden, in May 2017.This study compares the numerical predictions of the wake flow behind a model wind turbine operated in yaw to experimental wind tunnel results. Prior to the work shop, research groups were invited to predict the turbines' performances and wake flow properties using computational fluid dynamics (CFD) methods. For this purpose, the power, thrust and yaw moments for a 30 • yawed model turbine as well as the wake's mean and turbulent streamwise and vertical flow components were measured 5 in the wind tunnel at the Norwegian University of Science and Technology (NTNU). In order to increase the complexity, a non-yawed downstream turbine was added in a second test case, while a third test case challenged the modelers with a new rotor and turbine geometry. Four participants submitted predictions using different flow solvers, three of which were based on Large Eddy Simulations (LES) while another one used an Improved Delayed Detached Eddy Simulation (IDDES) model. The performance of a single 10 yawed turbine was fairly well predicted by all simulations, both in the first and third test case. The scatter in the downstream turbine's performance predictions in the second test case, however, was found to be significantly larger. The complex asymmetric shape of the mean streamwise and vertical velocity was generally well predicted by all the simulations for all test cases.The largest improvement with respect to previous Blind tests is the good prediction of the levels of turbulent kinetic energy in the wake, even for the complex case of yaw misalignment. These very promising results confirm the mature development 15 stage of LES/DES simulations for wind turbine wake modeling, while competitive advantages might be obtained by faster computational methods.

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From wake steering to flow control

Cited by 5 publications

References 9 publications

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

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

Instantaneous Response and Mutual Interaction between Wind Turbine and Flow

Blind test comparison on the wake behind a yawed wind turbine

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