Measuring a Utility-Scale Turbine Wake Using the TTUKa Mobile Research Radars

Hirth, Brian D.; Schroeder, John L.; Gunter, W. Scott; Guynes, Jerry

doi:10.1175/jtech-d-12-00039.1

Cited by 59 publications

(57 citation statements)

References 6 publications

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“…Remote sensing technologies such as sodars, lidars, and radars can provide measurements of some important of atmospheric parameters in the vicinity of operating turbines (e.g., H€ ogstr€ om et al (1988); Barthelmie et al (2003); Nygaard (2011);Hirth et al (2012); Rhodes and Lundquist (2013); Smalikho et al (2013);and Iungo and Port e-Agel (2013)). While these technologies are highly transportable, permitting the sampling of flow at multiple locations, they typically sample either over large volumes or at low temporal frequencies, which can hinder the interpretation of turbulence phenomena (Sathe et al, 2011), or sample in unique, platform-dependent manners, which must be accounted for to enable proper interpretation.…”

Section: Journal Of Renewable and Sustainable Energymentioning

confidence: 99%

“…Aitken and Lundquist (2014) used observations from a nacelle-mounted scanning lidar to characterize the turbine's wake as it evolves downwind, finding that both the wake expansion rate and the amplitude of wake meandering are greater during high ambient turbulence intensity and daytime conditions, as compared to low turbulence and nocturnal conditions. Dual-Doppler radar (Hirth et al, 2012) has enabled documentation of turbine wakes extending to 30D downwind.…”

Section: Journal Of Renewable and Sustainable Energymentioning

confidence: 99%

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Investigating wind turbine impacts on near-wake flow using profiling lidar data and large-eddy simulations with an actuator disk model

Mirocha

Rajewski

Marjanovic

et al. 2015

Journal of Renewable and Sustainable Energy

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Wind turbine impacts on the atmospheric flow are investigated using data from the Crop Wind Energy Experiment (CWEX-11) and large-eddy simulations (LESs) utilizing a generalized actuator disk (GAD) wind turbine model. CWEX-11 employed velocity-azimuth display (VAD) data from two Doppler lidar systems to sample vertical profiles of flow parameters across the rotor depth both upstream and in the wake of an operating 1.5 MW wind turbine. Lidar and surface observations obtained during four days of July 2011 are analyzed to characterize the turbine impacts on wind speed and flow variability, and to examine the sensitivity of these changes to atmospheric stability. Significant velocity deficits (VD) are observed at the downstream location during both convective and stable portions of four diurnal cycles, with large, sustained deficits occurring during stable conditions. Variances of the streamwise velocity component, σu, likewise show large increases downstream during both stable and unstable conditions, with stable conditions supporting sustained small increases of σu, while convective conditions featured both larger magnitudes and increased variability, due to the large coherent structures in the background flow. Two representative case studies, one stable and one convective, are simulated using LES with a GAD model at 6 m resolution to evaluate the compatibility of the simulation framework with validation using vertically profiling lidar data in the near wake region. Virtual lidars were employed to sample the simulated flow field in a manner consistent with the VAD technique. Simulations reasonably reproduced aggregated wake VD characteristics, albeit with smaller magnitudes than observed, while σu values in the wake are more significantly underestimated. The results illuminate the limitations of using a GAD in combination with coarse model resolution in the simulation of near wake physics, and validation thereof using VAD data.

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Section: Journal Of Renewable and Sustainable Energymentioning

confidence: 99%

Section: Journal Of Renewable and Sustainable Energymentioning

confidence: 99%

Investigating wind turbine impacts on near-wake flow using profiling lidar data and large-eddy simulations with an actuator disk model

Mirocha

Rajewski

Marjanovic

et al. 2015

Journal of Renewable and Sustainable Energy

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show abstract

“…These instruments can measure wind characteristics above the heights of most traditional meteorological towers, and they can be deployed and moved rather easily, allowing measurements at several different locations. Many wake validation studies from remote sensing measurements focus on individual isolated turbines (Käsler et al, 2010;Bingöl et al, 2010;Trujillo et al, 2011;Hirth et al, 2012;Hirth and Schroeder, 2013;Aitken et al, 2014a;Aitken and Lundquist, 2014;Bastine et al, 2015;Kumer et al, 2015), with some studies that aim to reconstruct the three-dimensional structure of wind turbine wakes (Iungo et al, 2013;Banta et al, 2015). The interactions between multiple wakes must be captured in studies of large wind farms, as done by Clive et al (2011), Hirth et al (2015a, Kumer et al (2015), Wang and Barthelmie (2015), Aubrun et al (2016), andvan Dooren et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

2017

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Abstract. The lower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions. Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. These insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.

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“…Normally, the wakes are invisible, discernible only through the reduced production of downstream turbines caught in the wakes. But in recent years, wakes have been visualized and put under quantitative scrutiny (both onshore and offshore) through the deployment of remote sensing methods such as lidars [10][11][12], radars [13] and synthetic aperture radar [14,15].…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Wake: The 2016 Horns Rev Photo Case

et al. 2017

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Offshore wind farm wakes were observed and photographed in foggy conditions at Horns Rev 2 on 25 January 2016 at 12:45 UTC. These new images show highly contrasting conditions regarding the wind speed, turbulence intensity, atmospheric stability, weather conditions and wind farm wake development as compared to the Horns Rev 1 photographs from 12 February 2008. The paper examines the atmospheric conditions from satellite images, radiosondes, lidar and wind turbine data and compares the observations to results from atmospheric meso-scale modelling and large eddy simulation. Key findings are that a humid and warm air mass was advected from the southwest over cold sea and the dew-point temperature was such that cold-water advection fog formed in a shallow layer. The flow was stably stratified and the freestream wind speed was 13 m/s at hub height, which means that most turbines produced at or near rated power. The wind direction was southwesterly and long, narrow wakes persisted several rotor diameters downwind of the wind turbines. Eventually mixing of warm air from aloft dispersed the fog in the far wake region of the wind farm.

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Measuring a Utility-Scale Turbine Wake Using the TTUKa Mobile Research Radars

Cited by 59 publications

References 6 publications

Investigating wind turbine impacts on near-wake flow using profiling lidar data and large-eddy simulations with an actuator disk model

Investigating wind turbine impacts on near-wake flow using profiling lidar data and large-eddy simulations with an actuator disk model

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

Wind Farm Wake: The 2016 Horns Rev Photo Case

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