Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements

Doubrawa, Paula; Barthelmie, R.J.; Wang, Hui; Pryor, Sara C.; Churchfield, Matthew J.

doi:10.3390/rs8110939

Cited by 22 publications

(14 citation statements)

References 35 publications

(54 reference statements)

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“…-Selecting sufficient elevation angles is necessary for providing sufficient detail of the wake at various heights and distances (Doubrawa et al, 2017), but increasing the number of elevation angles increases the disjunct time interval.…”

Section: Defining Lidar Scan Geometrymentioning

confidence: 99%

Automated wind turbine wake characterization in complex terrain

Barthelmie

Pryor

2019

Atmos. Meas. Tech.

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Abstract. An automated wind turbine wake characterization algorithm has been developed and applied to a data set of over 19 000 scans measured by a ground-based scanning Doppler lidar at Perdigão, Portugal, over the period January to June 2017. Potential wake cases are identified by wind speed, direction and availability of a retrieved free-stream wind speed. The algorithm correctly identifies the wake centre position in 62 % of possible wake cases, with 46 % having a clear and well-defined wake centre surrounded by a coherent area of lower wind speeds while 16 % have split centres or multiple lobes where the lower wind speed volumes are no longer in coherent areas but present as two or more distinct areas or lobes. Only 5 % of cases are not detected; the remaining 33 % could not be categorized either by the algorithm or subjectively, mainly due to the complexity of the background flow. Average wake centre heights categorized by inflow wind speeds are shown to be initially lofted (to two rotor diameters, D, downstream) except when the inflow wind speeds exceed 12 ms−1. Even under low wind speeds, by 3.5 D downstream of the wind turbine, the mean wake centre position is below the initial wind turbine hub height and descends broadly following the terrain slope. However, this behaviour is strongly linked to the hour of the day and atmospheric stability. Overnight and in stable conditions, the average height of the wake centre is 10 m higher than in unstable conditions at 2 D downstream from the wind turbine and 17 m higher at 4.5 D downstream.

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Section: Defining Lidar Scan Geometrymentioning

confidence: 99%

Automated wind turbine wake characterization in complex terrain

Barthelmie

Pryor

2019

Atmos. Meas. Tech.

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“…The wake center is estimated for a given downwind turbine and based on the estimation, the yaw angle of the upwind turbine is varied. Doubrawa et al have carried out experimental studies on wind turbine wake characterization based on LiDAR measurements [11]. Results reveal that the wind speed measurements carried out using LES data are found to be in good approximation with those from LiDAR.…”

Section: Introductionmentioning

confidence: 99%

Wake Management in Wind Farms: An Adaptive Control Approach

et al. 2019

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Advanced wind measuring systems like Light Detection and Ranging (LiDAR) is useful for wake management in wind farms. However, due to uncertainty in estimating the parameters involved, adaptive control of wake center is needed for a wind farm layout. LiDAR is used to track the wake center trajectory so as to perform wake control simulations, and the estimated effective wind speed is used to model wind farms in the form of transfer functions. A wake management strategy is proposed for multi-wind turbine system where the effect of upstream turbines is modeled in form of effective wind speed deficit on a downstream wind turbine. The uncertainties in the wake center model are handled by an adaptive PI controller which steers wake center to desired value. Yaw angle of upstream wind turbines is varied in order to redirect the wake and several performance parameters such as effective wind speed, velocity deficit and effective turbulence are evaluated for an effective assessment of the approach. The major contributions of this manuscript include transfer function based methodology where the wake center is estimated and controlled using LiDAR simulations at the downwind turbine and are validated for a 2-turbine and 5-turbine wind farm layouts.

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“…Kumer et al [8] focused on characterization of turbulence in the wake using lidar. Doubrowa et al [9] focused on the uncertainty on mean winds within wind farm wake using lidar, while Dooren et al [10] presented a methodology to reconstruct the 2D horizontal wind fields in wind farm wake based on lidars.…”

Section: Introductionmentioning

confidence: 99%

Editorial for the Special Issue “Remote Sensing of Atmospheric Conditions for Wind Energy Applications”

Hasager

Sjöholm

2019

Remote Sensing

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This Special Issue hosts papers on aspects of remote sensing for atmospheric conditions for wind energy applications. The wind lidar technology is presented from a theoretical view on the coherent focused Doppler lidar principles. Furthermore, wind lidar for applied use for wind turbine control, wind farm wake, and gust characterizations are presented, as well as methods to reduce uncertainty when using lidar in complex terrain. Wind lidar observations are used to validate numerical model results. Wind Doppler lidar mounted on aircraft used for observing winds in hurricane conditions and Doppler radar on the ground used for very short-term wind forecasting are presented. For the offshore environment, floating lidar data processing is presented as well as an experiment with wind-profiling lidar on a ferry for model validation. Assessments of wind resources in the coastal zone using wind-profiling lidar and global wind maps using satellite data are presented.

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Wind Turbine Wake Characterization from Temporally Disjunct 3-D Measurements

Cited by 22 publications

References 35 publications

Automated wind turbine wake characterization in complex terrain

Automated wind turbine wake characterization in complex terrain

Wake Management in Wind Farms: An Adaptive Control Approach

Editorial for the Special Issue “Remote Sensing of Atmospheric Conditions for Wind Energy Applications”

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