Lidar estimation of rotor-effective wind speed – an experimental comparison

Abstract. Turbulence velocity spectra are of high importance for the estimation of loads on wind turbines and other built structures, as well as for fitting measured turbulence values to turbulence models. Spectra generated from reconstructed wind vectors of Doppler beam swinging (DBS) wind lidars differ from spectra based on one-point measurements. Profiling wind lidars have several characteristics that cause these deviations, namely cross-contamination between the three velocity components, averaging along the lines of sight and the limited sampling frequency. This study focuses on analyzing the cross-contamination effect. We sample wind data in a computer-generated turbulence box to predict lidar-derived turbulence spectra for three wind directions and four measurement heights. The data are then processed with the conventional method and with the method of squeezing that reduces the longitudinal separation distances between the measurement locations of the different lidar beams by introducing a time lag into the data processing. The results are analyzed and compared to turbulence velocity spectra from field measurements with a Windcube V2 wind lidar and ultrasonic anemometers as reference. We successfully predict lidar-derived spectra for all test cases and found that their shape is dependent on the angle between the wind direction and the lidar beams. With conventional processing, cross-contamination affects all spectra of the horizontal wind velocity components. The method of squeezing improves the spectra to an acceptable level only for the case of the longitudinal wind velocity component and when the wind blows parallel to one of the lines of sight. The analysis of the simulated spectra described here improves our understanding of the limitations of turbulence measurements with DBS profiling wind lidar.

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“…This has previously been reported by Mann (1994, Fig. 7a) and in Held and Mann (2019, their Fig. C1).…”

Section: Non-aligned Inflowsupporting

confidence: 90%

Cross-contamination effect on turbulence spectra from Doppler beam swinging wind lidar

Kelberlau

Mann

2020

Wind Energ. Sci.

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“…This will become the main source of the uncertainty in the power and load prediction. For example, the rotor-effective wind speed, an important quantity for pitch controller [68] defined as the average longitudinal wind speed component over the entire rotor plane, will be obviously disturbed by this phenomenon. In addition, spectrum analysis shows that the meandering phenomenon is comprised of two parts respectively attributed to the shed vortex and the large-scale inflow structures.…”

Section: Discussionmentioning

confidence: 99%

LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics

Wan

2019

Sustainability

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Wake meandering disturbs the stability of the far wake field and thus increases the fatigue loads of downstream wind turbines. A deep understanding of this phenomenon under atmospheric boundary layers and its relation to the structural loads helps to better model the dynamic wake and alleviate adverse effects. A large eddy simulation and an actuator line model are introduced in the present work to simulate the wake field and aerodynamic loads of wind turbines with different longitudinal spacings. By temporal filtering and the gaussian fitting method, the wake center and edge are precisely defined, and the dynamic wake characteristics, including the wake width, oscillation amplitude, and frequency, are described based on the statistical data of the simulated flow field. Results reveal that the wake meandering is caused by both large-scale atmospheric structure and the unstable vortex shed from the rotor because two distinct meandering frequency ranges are detected. As the atmosphere instability increases, the former becomes the dominant inducing factor of the meandering movements. Further, the analysis of the correlation between the inflow characteristics and the wake deflection shows that the Taylor hypothesis remains valid within a distance of over a thousand meters under both neutral and convective boundary layers, proving the feasibility of using this hypothesis for wake evolution prediction. In addition, our study shows that the fluctuation of blade root moment and yaw moment is significantly intensified by the meandering wake, with their standard deviation is augmenting by over two times under both atmospheric conditions. The power spectrum illustrates that the component with rotor rotation frequency of the former is sensible to the wake effect, but for the latter, the power spectrum density of all frequencies is increased under the meandering wake. These indicate that the fatigue loads will be underestimated without considering the wake meandering effect. Moreover, the high correlation between the wake deflection and yaw moment implies that we can predict yaw moment based on the incoming flow information with high accuracy.

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“…The CW and PL lidars differ in the emission waveform, and in the temporal and spatial resolution, among others (Peña et al, 2015). The Windar Photonics 2-and 4-beam CW lidars have been applied for wake detection purposes (Held and Mann, 2019a) and rotor-effective wind speed estimation (Held and Mann, 2019b). The ZephIR Dual Mode (DM) circular-scanning CW lidar, with a single beam and sampling frequency of approximately 50 Hz, has been used for several purposes including power curve assessment (Medley et al, 2014), wind field reconstruction (Borraccino et al, 2017), turbulence characterization (Peña et al, 2017), and load validation in both free and wake conditions (Dimitrov et al, 2019;Conti et al, 2020).…”

Section: Lidar Scanning Strategiesmentioning

confidence: 99%

Wind turbine load validation in wakes using field reconstruction techniques and nacelle lidar wind retrievals

Conti¹,

Pettas²,

Dimitrov³

et al. 2020

Preprint

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Abstract. This study proposes two methodologies for improving the accuracy of wind turbine load assessment under wake conditions by combining nacelle-mounted lidar measurements with wake wind field reconstruction techniques. The first approach consists in incorporating wind measurements of the wake flow field, obtained from nacelle lidars, into random, homogeneous Gaussian turbulence fields generated using the Mann spectral tensor model. The second approach imposes wake deficit time-series, which are derived by fitting a bivariate Gaussian shape function on lidar observations of the wake field, on the Mann turbulence fields. The two approaches are numerically evaluated using a virtual lidar simulator, which scans the wake flow fields generated with the Dynamic Wake Meandering (DWM) model. The lidar-reconstructed wake fields are input to aeroelastic simulations of the DTU 10 MW wind turbine and the resulting load predictions are compared with loads obtained with the target (no lidar-based) DWM simulated fields. The accuracy of load predictions is estimated across a variety of lidar beam configurations, probe volume sizes, and atmospheric turbulence conditions. The results indicate that the 10-min power and fatigue load statistics, predicted with lidar-reconstructed fields, are comparable with results obtained with the DWM simulations. Furthermore, the simulated power and load time-series exhibit a high level of correlation with the target observations, thus decreasing the statistical uncertainty (realization-to-realization) by a factor between 1.2 and 5, compared to results obtained with the baseline, which is DWM simulated fields with different random seeds. Finally, we show that the spatial resolutions of the lidar's scanning strategies as well as the size of the probe volume are critical aspects for the accuracy of the reconstructed wake fields and load predictions.

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Lidar estimation of rotor-effective wind speed – an experimental comparison

Cited by 38 publications

References 34 publications

Cross-contamination effect on turbulence spectra from Doppler beam swinging wind lidar

Cross-contamination effect on turbulence spectra from Doppler beam swinging wind lidar

LES Study of Wake Meandering in Different Atmospheric Stabilities and Its Effects on Wind Turbine Aerodynamics

Wind turbine load validation in wakes using field reconstruction techniques and nacelle lidar wind retrievals

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