Interaction of Wind Turbine Wakes under Various Atmospheric Conditions

Lee, S.; Vorobieff, Peter; Poroseva, Svetlana V.

doi:10.3390/en11061442

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…As a general rule of thumb [35,36], the stronger the ambient turbulent intensity, the faster the wake recovery. Some wind tunnel experiments and CFD simulations [37,38] have shown that the turbulence intensity in the wake overlay area is higher than that of the undisturbed wind turbine at the same location. Meanwhile, as mentioned above, for a particular experiment of two turbines in a row, the wake velocity of the downwind turbine recovers Energies 2019, 12, 680 5 of 14 more rapidly than that of the upwind one.…”

Section: Wake Superpositionmentioning

confidence: 99%

Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes

Shao

et al. 2019

Energies

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In a wind farm some wind turbines may be affected by multiple upwind wakes. The commonly used approach in engineering to simulate the interaction effect of different wakes is to combine the single analytical wake model and the interaction model. The higher turbulence level and shear stress profile generated by upwind turbines in the superposed area leads to faster wake recovery. The existing interaction models are all analytical models based on some simple assumptions of superposition, which cannot characterize this phenomenon. Therefore, in this study, a mixing coefficient is introduced into the classical energy balance interaction model with the aim of reflecting the effect of turbulence intensity on velocity recovery in multiple wakes. An empirical expression is also given to calculate this parameter. The performance of the new model is evaluated using data from the Lillgrund and the Horns Rev I offshore wind farms, and the simulations agree reasonably with the observations. The comparison of different interaction model simulation results with measured data show that the calculation accuracy of this new interaction model is high, and the mean absolute percentage error of wind farm efficiency is reduced by 5.3% and 1.58%, respectively, compared to the most commonly used sum of squares interaction model.

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Section: Wake Superpositionmentioning

confidence: 99%

Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes

Shao

et al. 2019

Energies

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“…Wakes of large offshore wind farm clusters over distances of more than 10 km were first observed using data from satellite SAR (Christiansen and Hasager, 2005). Li and Lehner (2013) and Hasager et al (2015) analysed offshore wind farm wakes using SAR images and compared the long, visible wakes to results of mesoscale models. Nygaard and Hansen (2016) analysed the power production of an offshore wind farm before and after the commissioning of a wind farm located 3 km to the west on the basis of SCADA data and discovered power losses caused by wakes of the upstream wind farm in the first rows of the downstream wind farm.…”

Section: Introductionmentioning

confidence: 99%

Cluster wakes impact on a far-distant offshore wind farm's power

et al. 2020

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Abstract. Our aim with this paper was the analysis of the influence of offshore cluster wakes on the power of a far-distant wind farm. We measured cluster wakes with long-range Doppler light detection and ranging (lidar) and satellite synthetic aperture radar (SAR) in different atmospheric stabilities and analysed their impact on the 400 MW offshore wind farm Global Tech I in the German North Sea using supervisory control and data acquisition (SCADA) power data. Our results showed clear wind speed deficits that can be related to the wakes of wind farm clusters up to 55 km upstream in stable and weakly unstable stratified boundary layers resulting in a clear reduction in power production. We discussed the influence of cluster wakes on the power production of a far-distant wind farm, cluster wake characteristics and methods for cluster wake monitoring. In conclusion, we proved the existence of wake shadowing effects with resulting power losses up to 55 km downstream and encouraged further investigations on far-reaching wake shadowing effects for optimized areal planning and reduced uncertainties in offshore wind power resource assessment.

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“…in warm air over cold water, wake deficits can last far downstream. Hansen et al (2011), Dörenkämper et al (2015) and Lee et al (2018) investigated wake recovery with respect to atmospheric stability and found an increased length of wakes in stable stratification. Emeis (2009), Turner et al (2014) and Schmidt and Stoevesandt (2015) suggested optimized wind farm layouts to reduce wake effects on the basis of the prevailing wind rose and stability distribution.…”

Section: Introductionmentioning

confidence: 99%

Cluster wakes impact on a far distant offshore wind farm's power

Schneemann¹,

Rott²,

Dörenkämper³

et al. 2019

Preprint

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Abstract. Our aim with this paper was the analysis of the influence of offshore cluster wakes on the power of a far distant wind farm. We measured cluster wakes with long range Doppler light detection and ranging (lidar) and satellite synthetic aperture radar (SAR) in different atmospheric stabilities and analysed their impact on the 400 MW offshore wind farm "Global Tech I" in the German North Sea using supervisory control and data acquisition (SCADA) power data. Our results showed clear wind speed deficits that can be related to the wakes of wind farm clusters up to 55 km upstream in stable and weakly unstable stratified boundary layers resulting in a clear reduction in power production. We discussed the influence of cluster wakes on the power production of a far distant wind farm, cluster wake characteristics and methods for cluster wake monitoring. In conclusion, we proved the existence of wake shadowing effects with resulting power losses up to 55 km downstream and encouraged further investigations on far reaching wake shadowing effects for optimized areal planning and reduced uncertainties in offshore wind power resource assessment.

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Interaction of Wind Turbine Wakes under Various Atmospheric Conditions

Cited by 8 publications

References 27 publications

Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes

Multiple Wind Turbine Wakes Modeling Considering the Faster Wake Recovery in Overlapped Wakes

Cluster wakes impact on a far-distant offshore wind farm's power

Cluster wakes impact on a far distant offshore wind farm's power

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