Evaluation of three lidar scanning strategies for turbulence measurements

Newman, Jennifer F.; Klein, Petra M.; Wharton, Sonia; Sathe, Ameya; Bonin, Timothy A.; Chilson, Phillip B.; Muschinski, Andreas

doi:10.5194/amt-9-1993-2016

Cited by 64 publications

(91 citation statements)

References 34 publications

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“…In this study we demonstrate that σ 2 u can be estimated by FL nacelle lidars, and current research demonstrates that lidar-based σ 2 u values reduce the gap between loads and power measurements, as well as simulations. It is difficult to compare our results with those from previous work on lidar turbulence measurements Newman et al, 2016). First, with a FL lidar we are able to point the beam in a direction close to the mean wind, whereas most lidars use beams pointing closer to the vertical wind component.…”

Section: Discussioncontrasting

confidence: 50%

Turbulence characterization from a forward-looking nacelle lidar

2017

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Abstract. We present two methods to characterize turbulence in the turbine inflow using radial velocity measurements from nacelle-mounted lidars. The first uses a model of the three-dimensional spectral velocity tensor combined with a model of the spatial radial velocity averaging of the lidars, and the second uses the ensembleaveraged Doppler radial velocity spectrum. With the former, filtered turbulence estimates can be predicted, whereas the latter model-free method allows us to estimate unfiltered turbulence measures. Two types of forwardlooking nacelle lidars are investigated: a pulsed system that uses a five-beam configuration and a continuouswave system that scans conically. For both types of lidars, we show how the radial velocity spectra of the lidar beams are influenced by turbulence characteristics, and how to extract the velocity-tensor parameters that are useful to predict the loads on a turbine. We also show how the velocity-component variances and co-variances can be estimated from the radial-velocity unfiltered variances of the lidar beams. We demonstrate the methods using measurements from an experiment conducted at the Nørrekaer Enge wind farm in northern Denmark, where both types of lidars were installed on the nacelle of a wind turbine. Comparison of the lidar-based along-wind unfiltered variances with those from a cup anemometer installed on a meteorological mast close to the turbine shows a bias of just 2 %. The ratios of the unfiltered and filtered radial velocity variances of the lidar beams to the cup-anemometer variances are well predicted by the spectral model. However, other lidar-derived estimates of velocity-component variances and co-variances do not agree with those from a sonic anemometer on the mast, which we mostly attribute to the small cone angle of the lidar. The velocity-tensor parameters derived from sonic-anemometer velocity spectra and those derived from lidar radial velocity spectra agree well under both near-neutral atmospheric stability and high wind-speed conditions, with differences increasing with decreasing wind speed and increasing stability. We also partly attribute these differences to the lidar beam configuration.

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Section: Discussioncontrasting

confidence: 50%

Turbulence characterization from a forward-looking nacelle lidar

2017

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“…However, due to the equations for computing each component of the Reynolds stress tensor, the effect of noise within each individual measurement is magnified. In particular, if there is a large amount of noise in the vertical beam compared to other beams from differences in SNR, then negative values of σ 2 u and σ 2 v can be computed (Newman et al, 2016), which is not realistic. Thus, if the observations within each beam are taken at a quick enough sampling rate to resolve the inertial subrange, the autocovariance technique should be applied to variances calculated from each beam before computing the velocity variances.…”

Section: Possible Applications To Other DL Scanning Techniquesmentioning

confidence: 99%

“…Several different DL scanning strategies outlined in Sect. 1 were tested for comparison of turbulence measurements with those collected from sonic anemometers, which have been compared by (Newman et al, 2016). Herein, we focus on measurements taken between 17:00 UTC (11:00 local standard time, LST) 26 March and 15:00 UTC (09:00 LST) 28 March, when two DLs were placed within 2 m of the sonic anemometer booms on the 300 m tower.…”

Section: Experiments and Instrumentationmentioning

confidence: 99%

Improvement of vertical velocity statistics measured by a Doppler lidar through comparison with sonic anemometer observations

et al. 2016

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Abstract. Since turbulence measurements from Doppler lidars are being increasingly used within wind energy and boundary-layer meteorology, it is important to assess and improve the accuracy of these observations. While turbulent quantities are measured by Doppler lidars in several different ways, the simplest and most frequently used statistic is vertical velocity variance (w 2 ) from zenith stares. However, the competing effects of signal noise and resolution volume limitations, which respectively increase and decrease w 2 , reduce the accuracy of these measurements. Herein, an established method that utilises the autocovariance of the signal to remove noise is evaluated and its skill in correcting for volume-averaging effects in the calculation of w 2 is also assessed. Additionally, this autocovariance technique is further refined by defining the amount of lag time to use for the most accurate estimates of w 2 . Through comparison of observations from two Doppler lidars and sonic anemometers on a 300 m tower, the autocovariance technique is shown to generally improve estimates of w 2 . After the autocovariance technique is applied, values of w 2 from the Doppler lidars are generally in close agreement (R 2 ≈ 0.95 − 0.98) with those calculated from sonic anemometer measurements.

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“…Accordingly, uncertainties in lidar-derived mean wind velocity estimates have been well characterized Lindelöw-Marsden, 2009) and methods and procedures have been developed for error reduction and uncertainty control (Clifton et al, 2013;Gottschall et al, 2012). However, use of lidar for turbulence measurements, while possible (Newman et al, 2016;Branlard et al, 2013;Mann et al, 2010), is less established (Sathe et al, 2015;Sathe and Mann, 2013). Two methods are commonly used to derive the second-order moments (i.e., velocity variances and momentum fluxes) of turbulent flow from lidar data (Sathe and Mann, 2013).…”

Section: Motivation and Approachmentioning

confidence: 99%

“…The first method can suffer from cross-contamination errors (biases) due to the correlation between the radial velocities used for wind velocity estimates (Sathe et al, 2011). Therefore, the second method is considered to be more suitable for lidar turbulence measurements (Newman et al, 2016;Sathe et al, 2015;Sathe and Mann, 2013;Mann et al, 2010) Using the second method mentioned above, errors in the estimated variances and momentum fluxes are accumulations of the following three types of errors in the estimated radial velocity variance:…”

Section: Motivation and Approachmentioning

confidence: 99%

Errors in radial velocity variance from Doppler wind lidar

et al. 2016

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Abstract. A high-fidelity lidar turbulence measurement technique relies on accurate estimates of radial velocity variance that are subject to both systematic and random errors determined by the autocorrelation function of radial velocity, the sampling rate, and the sampling duration. Using both statistically simulated and observed data, this paper quantifies the effect of the volumetric averaging in lidar radial velocity measurements on the autocorrelation function and the dependence of the systematic and random errors on the sampling duration. For current-generation scanning lidars and sampling durations of about 30 min and longer, during which the stationarity assumption is valid for atmospheric flows, the systematic error is negligible but the random error exceeds about 10 %.

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Evaluation of three lidar scanning strategies for turbulence measurements

Cited by 64 publications

References 34 publications

Turbulence characterization from a forward-looking nacelle lidar

Turbulence characterization from a forward-looking nacelle lidar

Improvement of vertical velocity statistics measured by a Doppler lidar through comparison with sonic anemometer observations

Errors in radial velocity variance from Doppler wind lidar

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