Lidar Investigation of Atmosphere Effect on a Wind Turbine Wake

Smalikho, Igor N.; Banakh, V. A.; Pichugina, Yelena L.; Brewer, W. Alan; Banta, Robert M.; Lundquist, Julie K.; Kelley, Neil

doi:10.1175/jtech-d-12-00108.1

Cited by 88 publications

(71 citation statements)

References 30 publications

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“…Figure 3 shows that the wake is strongest in low-turbulence conditions 5 and dissipates quickly in high-turbulence conditions. This result is consistent with previous work investigating the effects of atmospheric conditions on wakes (Smalikho et al (2013)). Figure 4 shows how the controls-oriented engineering models presented in this paper compare with the lidar data.…”

Section: Atmospheric Conditionssupporting

confidence: 93%

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: Atmospheric Conditionssupporting

confidence: 93%

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

Annoni¹,

Fleming²,

Scholbrock³

et al. 2018

Preprint

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show abstract

“…More advanced scanning scenarios can be performed with instruments that have full hemispherical scanning capabilities and thus allow to scan arbitrary volumes of the atmosphere. Such systems 15 have been deployed in the past to measure wind turbine wakes by scanning through them either horizontally (in so-called plan-position Indicator mode, short: PPI), or vertically (in so-called range-height indicator mode, short: RHI) (Käsler et al, 2010;Smalikho et al, 2013) and thus showing their propagation path. A drawback of measurements with single lidars is that only radial wind velocities along the line-of-sight of the laser beam can be retrieved.…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Wake Measurements with Automatically Adjusting Scanning Trajectories in a Multi-Doppler Lidar Setup

Wildmann¹,

Vasiljević²,

Gerz³

2018

Preprint

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Abstract. In the context of the Perdigão 2017 experiment, the German Aerospace Center (DLR) deployed three long-range scanning Doppler lidars with the dedicated purpose of investigating the wake of a single wind turbine at the experimental site. A novel method was established to investigate wake properties with ground-based lidars over a wide range of wind directions. For this method, the three lidars, which were space-and time-synchronized using the WindScanner software, were programmed to measure with crossing beams at individual points up to ten rotor diameters downstream the wind turbine. Every half hour, the 5 measurement points were adapted to the current wind direction to obtain a high availability of wake measurements in changing wind conditions. The linearly independent radial velocities where the lidar beams intersect allow the calculation of the wind vector at those points. Two approaches to estimate the prevailing wind direction were tested throughout the campaign. In the first approach, VAD scans of one of the lidars were used to calculate a five-minute average of wind speed and wind direction every half hour, whereas later in the experiment, five-minute averages of sonic anemometer measurements of a meteorological 10 mast close to the wind turbine became available in real-time and were used for the scanning adjustment. Results of wind speed deficit measurements are presented for two measurement days with varying westerly winds and it is evaluated how well the lidar beam intersection points match the actual wake location. The new method allowed to obtain wake measurements over the whole measurement period, whereas a static scanning setup would only have captured short periods of wake occurrences. The analysed cases reveal that state-of-the-art engineering models for wakes underestimate the actual wind speed deficit.

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“…During the last decade, in particular, wind LiDAR systems have massively entered the research field of wind energy (e.g., [4][5][6][7][8]). Corresponding applications for turbine wake investigations have so far been focusing on the average wake structure, e.g., for the test and validation of simple wake models (e.g., [9]), wake recovery and downstream expansion (e.g., [2,10,11]) or wake meandering (e.g., [12]), in dependence of atmospheric stability.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Turbulence in Wind Turbine Wakes under Different Stability Conditions from Static Doppler LiDAR Measurements

2017

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Wake characteristics are of great importance for wind park performance and turbine loads. While wind tunnel experiments provided a solid base for the basic understanding of the structure and dynamics of wind turbine wakes, the consequent step forward to characterize wakes is full-scale measurements in real atmospheric boundary layer conditions under different stability regimes. Scanning Doppler LiDAR measurements have proven to be a flexible and useful tool for such measurements. However, their advantage of measuring spatial fluctuation is accompanied by the limited temporal resolution of individual sampling volumes within the scanned area. This study presents results from LiDAR Doppler Beam Swing (DBS) measurements and highlights the potential of information retrieved from a spectral analysis of wake measurements. Data originate from three Windcube v1 and sonic anemometers, collected during the Wind Turbine Wake Experiment-Wieringermeer. Despite the ongoing research on the reliability of turbulence retrievals based on DBS data, our results show wake peak frequencies consistent with sonic anemometer measurements. The energy spectra show rather distinct maxima during stable conditions, which broaden during unstable and neutral conditions. Investigations on the effect of blade pitch on downstream wind speed and turbulence intensity profiles indicate the potential for the development of stability-dependent wind farm control strategies.

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Lidar Investigation of Atmosphere Effect on a Wind Turbine Wake

Cited by 88 publications

References 30 publications

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

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

Wind Turbine Wake Measurements with Automatically Adjusting Scanning Trajectories in a Multi-Doppler Lidar Setup

Characterization of Turbulence in Wind Turbine Wakes under Different Stability Conditions from Static Doppler LiDAR Measurements

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