Wind Measurements from Arc Scans with Doppler Wind Lidar

Wang, Hui; Barthelmie, R.J.; Clifton, A.; Pryor, S. C.

doi:10.1175/jtech-d-14-00059.1

Cited by 39 publications

(57 citation statements)

References 39 publications

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“…Use of arc scans adds two additional parameters to the scanning geometry: (1) the arc span (i.e., the width of the scan sector, θ ) and (2) the angle between the center of the arc and the wind direction, which is a measure of the orientation of the arc scan and will be called hereafter the relative direction and denoted as β. The selection of these parameters have implications for the accuracy of the retrieved wind speed (Courtney et al, 2014;Wang et al, 2015). Here we extend prior work on optimizing scan geometry to minimize the uncertainty in the estimated wind speeds.…”

Section: Introductionmentioning

confidence: 86%

“…The gray dots show the observed radial velocity variance approximated by the difference between the variance and the autocovariance at one time lag of the radial velocities collected in an experiment in which the lidar was operated with a staring mode at the US National Renewable Energy Laboratory (Wang et al, 2015), and the observed CramerRao Bound (CRB) (filled dark circles) is approximated by the mean of the lowest 5 % of the gray dots (Frehlich, 2001). The empirical relationship between the SNR and the CRB is denoted by the dark solid line and is based on Eq.…”

Section: Uncertainty In the Lidar Radial Velocitymentioning

confidence: 99%

“…Assuming a horizontally homogeneous wind field with zero mean vertical wind speed (i.e., w 0 = 0), the solution of the ordinary least squares (V l ) based on Eq. (1) is the estimate of horizontal wind velocity (Wang et al, 2015):…”

Section: Uncertainty In Wind Speeds Derived From Lidar Arc Scan Measumentioning

confidence: 99%

“…assuming zero random error for radial velocity (Wang et al, 2015). The variance of the random error σ 2 e is ∼ 0.01 m 2 s −2 (see Sect.…”

Section: Uncertainty In Wind Speeds Derived From Lidar Arc Scan Measumentioning

confidence: 99%

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Lidar arc scan uncertainty reduction through scanning geometry optimization

Wang¹,

Barthelmie²,

Pryor³

et al. 2016

Atmos. Meas. Tech.

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Abstract. Doppler lidars are frequently operated in a mode referred to as arc scans, wherein the lidar beam scans across a sector with a fixed elevation angle and the resulting measurements are used to derive an estimate of the n minute horizontal mean wind velocity (speed and direction). Previous studies have shown that the uncertainty in the measured wind speed originates from turbulent wind fluctuations and depends on the scan geometry (the arc span and the arc orientation). This paper is designed to provide guidance on optimal scan geometries for two key applications in the wind energy industry: wind turbine power performance analysis and annual energy production prediction. We present a quantitative analysis of the retrieved wind speed uncertainty derived using a theoretical model with the assumption of isotropic and frozen turbulence, and observations from three sites that are onshore with flat terrain, onshore with complex terrain and offshore, respectively. The results from both the theoretical model and observations show that the uncertainty is scaled with the turbulence intensity such that the relative standard error on the 10 min mean wind speed is about 30 % of the turbulence intensity. The uncertainty in both retrieved wind speeds and derived wind energy production estimates can be reduced by aligning lidar beams with the dominant wind direction, increasing the arc span and lowering the number of beams per arc scan. Large arc spans should be used at sites with high turbulence intensity and/or large wind direction variation.

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Section: Introductionmentioning

confidence: 86%

Section: Uncertainty In the Lidar Radial Velocitymentioning

confidence: 99%

Section: Uncertainty In Wind Speeds Derived From Lidar Arc Scan Measumentioning

confidence: 99%

“…assuming zero random error for radial velocity (Wang et al, 2015). The variance of the random error σ 2 e is ∼ 0.01 m 2 s −2 (see Sect.…”

Section: Uncertainty In Wind Speeds Derived From Lidar Arc Scan Measumentioning

confidence: 99%

See 2 more Smart Citations

Lidar arc scan uncertainty reduction through scanning geometry optimization

Wang¹,

Barthelmie²,

Pryor³

et al. 2016

Atmos. Meas. Tech.

View full text Add to dashboard Cite

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“…A basic spike filter was evaluated for the model in addition to several methods developed by Lenschow et al (2000). The spike filter routine used in this work was based on one of the lidar preprocessing steps presented by Wang et al (2015). First, the difference between adjacent velocity values, v, is calculated for all velocity measurements in a 10 min period, defined by the following equation:…”

Section: Instrument Noisementioning

confidence: 99%

An error reduction algorithm to improve lidar turbulence estimates for wind energy

Newman

Clifton

2017

Wind Energ. Sci.

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Abstract. Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry.In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices.L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physicsbased corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

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