SWiFT site atmospheric characterization

Kelley, Christopher; Ennis, Brandon Lee

doi:10.2172/1237403

Cited by 39 publications

(48 citation statements)

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“…Scaled Wind Farm Technology facility layout. The turbines' wake is visualized for the incoming wind direction 184° S, which is the prevailing direction at the SWiFT site [Colour figure can be viewed at wileyonlinelibrary.com]…”

Section: Configurationmentioning

confidence: 99%

“…In this paper, the performance of NESC is evaluated using large‐eddy simulations (LESs), conducted with our in‐house code University of Texas at Dallas‐Wind Farm model (UTD‐WF) . Large‐eddy simulations reproduce a three‐turbine wind farm, the Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas . This research facility consists of three small‐scale wind turbines with rated capacity 300 kW and rotor diameter 27 m. Modern power plants use larger, multimegawatt wind turbines, and the trend is to further increase the rotor size .…”

Section: Introductionmentioning

confidence: 99%

“…The velocity profile with shear has been obtained from a precursor simulation with periodic boundary conditions and cube roughness on the ground . The roughness height has been tuned to have a velocity profile similar to that typical at the SWiFT site . The uniform inflow case has been considered as an ideal reference scenario.…”

Section: Introductionmentioning

confidence: 99%

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Effect of the turbine scale on yaw control

2018

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Yaw misalignment between the incoming wind and the rotor of a turbine causes a lateral displacement of the wake. This effect can be exploited to avoid or mitigate wake interactions in wind farms, so that power losses are minimized. We performed large‐eddy simulations to evaluate yaw control for a three‐turbine wind farm. We used two different turbine models to assess how the size of the turbine rotor affects the farm efficiency and the effectiveness of the control strategy. A utility‐scale wind turbine with rotor diameter of 126 m is compared with a scaled research wind turbine with rotor diameter of 27 m. In both cases, a model‐free algorithm is used to determine the turbine yaw set point, which maximizes total power production. The algorithm is the nested extremum‐seeking control (NESC), which allows for the coordinated optimization of the wind turbine operating points. The results achieved with NESC are validated by computing a static performance map for different yaw angles. NESC converges to optimal operating conditions, which are in good agreement with the static map benchmark. Numerical results show that a larger rotor diameter induces larger wake deflection, thus achieving higher power improvements. From the analysis of the turbine structural loads, an increase in damage equivalent load is observed for both the yawed turbine and the waked one. Present results suggest that there is a cost‐effective trade‐off between performance and loads for large turbines.

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Section: Configurationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effect of the turbine scale on yaw control

2018

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“…Quantifying wind turbine inflow conditions and wake behavior is an important step to improving turbine performance and control and protecting the turbine from damaging unsteady loads. Until recently, wind measurements in the field were primarily captured with anemometer‐mounted meteorological towers (met towers) . The capability of met towers to measure turbine inflow and wake conditions is limited by several factors including permanent locality of measurements at a single coordinate point, maximum vertical position limited by tower height, and cost.…”

Section: Introductionmentioning

confidence: 99%

The spectral signature of wind turbine wake meandering: A wind tunnel and field‐scale study

2018

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Field‐scale and wind tunnel experiments were conducted in the 2D to 6D turbine wake region to investigate the effect of geometric and Reynolds number scaling on wake meandering. Five field deployments took place: 4 in the wake of a single 2.5‐MW wind turbine and 1 at a wind farm with numerous 2‐MW turbines. The experiments occurred under near‐neutral thermal conditions. Ground‐based lidar was used to measure wake velocities, and a vertical array of met‐mounted sonic anemometers were used to characterize inflow conditions. Laboratory tests were conducted in an atmospheric boundary layer wind tunnel for comparison with the field results. Treatment of the low‐resolution lidar measurements is discussed, including an empirical correction to velocity spectra using colocated lidar and sonic anemometer. Spectral analysis on the laboratory‐ and utility‐scale measurements confirms a meandering frequency that scales with the Strouhal number St = fD/U based on the turbine rotor diameter D. The scaling indicates the importance of the rotor‐scaled annular shear layer to the dynamics of meandering at the field scale, which is consistent with findings of previous wind tunnel and computational studies. The field and tunnel spectra also reveal a deficit in large‐scale turbulent energy, signaling a sheltering effect of the turbine, which blocks or deflects the largest flow scales of the incoming flow. Two different mechanisms for wake meandering—large scales of the incoming flow and shear instabilities at relatively smaller scales—are discussed and inferred to be related to the turbulent kinetic energy excess and deficit observed in the wake velocity spectra.

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“…Each meteorological tower possesses unique geometry and boom layout, so similar analyses must be completed for each tower under scrutiny. Analysis of sonic anemometer observations on the 200 m tower at the SWiFT site in Texas identified the wake of the tower frame in the increase in turbulence intensity at the sonic anemometers mounted on booms on a single side of the tower (Kelley and Ennis, 2016). At the Høvsøre site in Denmark, the meteorological tower has cup anemometers and wind vanes on the south booms and sonic anemometers on the north booms, but Peña et al (2016) state that tower distortions are generally neglected in meteorological studies, since the westerly and easterly winds are predominant and more homogenous.…”

Section: Introductionmentioning

confidence: 99%

Identification of tower-wake distortions using sonic anemometer and lidar measurements

et al. 2017

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Abstract. The eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) field campaign took place in March through May 2015 at the Boulder Atmospheric Observatory, utilizing its 300 m meteorological tower, instrumented with two sonic anemometers mounted on opposite sides of the tower at six heights. This allowed for at least one sonic anemometer at each level to be upstream of the tower at all times and for identification of the times when a sonic anemometer is in the wake of the tower frame. Other instrumentation, including profiling and scanning lidars aided in the identification of the tower wake. Here we compare pairs of sonic anemometers at the same heights to identify the range of directions that are affected by the tower for each of the opposing booms. The mean velocity and turbulent kinetic energy are used to quantify the wake impact on these first-and second-order wind measurements, showing up to a 50 % reduction in wind speed and an order of magnitude increase in turbulent kinetic energy. Comparisons of wind speeds from profiling and scanning lidars confirmed the extent of the tower wake, with the same reduction in wind speed observed in the tower wake, and a speed-up effect around the wake boundaries. Wind direction differences between pairs of sonic anemometers and between sonic anemometers and lidars can also be significant, as the flow is deflected by the tower structure. Comparisons of lengths of averaging intervals showed a decrease in wind speed deficit with longer averages, but the flow deflection remains constant over longer averages. Furthermore, asymmetry exists in the tower effects due to the geometry and placement of the booms on the triangular tower. An analysis of the percentage of observations in the wake that must be removed from 2 min mean wind speed and 20 min turbulent values showed that removing even small portions of the time interval due to wakes impacts these two quantities. However, a vast majority of intervals have no observations in the tower wake, so removing the full 2 or 20 min intervals does not diminish the XPIA dataset.

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SWiFT site atmospheric characterization

Cited by 39 publications

References 1 publication

Effect of the turbine scale on yaw control

Effect of the turbine scale on yaw control

The spectral signature of wind turbine wake meandering: A wind tunnel and field‐scale study

Identification of tower-wake distortions using sonic anemometer and lidar measurements

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