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Title: Evaluation of Wind Speed Retrieval from Continuous-Wave Lidar Measurements of a Wind Turbine Wake Using Virtual Lidar Techniques

Abstract

Wind lidar technology is being extensively used in utility-scale wind turbine wake measurements because of its capabilities to capture the dominant structures of interest in the near-wake region. However, wind lidar devices provide only line-of-sight (LOS) measurements, and retrieval of horizontal wind speed from the LOS measurements of a single lidar in the wind turbine wake could introduce errors due to the assumptions used in the retrieval process. With that in mind, the goals of this paper are to estimate the errors associated with the retrieval processes and provide guidelines for best practices. To achieve these goals, virtual lidar samples are obtained with large-eddy simulations (LES) by sampling the flow field with a model of the Technical University of Denmark (DTU) SpinnerLidar. LES is chosen to simulate the neutral and unstable atmospheric boundary layer (ABL), a benchmark case of the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) Task 31. The precursor ABL simulation is validated with the reference data provided by the IEA Wind Task 31 benchmark case. The data from the precursor simulation is used to drive a simulation of a wind turbine submerged in the ABL. A weighting function is applied to the LES datamore » to mimic the real DTU SpinnerLidar measurements. In the end, the virtual lidar data are processed and compared with the original LES data to estimate the errors. Results show that lidar captures mean ABL profile without any noticeable error, whereas turbulence intensity is underpredicted ~15% at hub height for the neutral case. Volume averaging by the lidar technology has a significant effect in the shear layer of a wind turbine wake.« less

Authors:
 [1];  [1];  [2];  [1];  [2];  [1]
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  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
OSTI Identifier:
1562860
Report Number(s):
NREL/JA-5000-73244
Journal ID: ISSN 1742-6588; TRN: US2000764
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Physics. Conference Series
Additional Journal Information:
Journal Volume: 1256; Journal ID: ISSN 1742-6588
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; 42 ENGINEERING; wind; lidar; wind speed retrieval; virtual lidar

Citation Formats

Debnath, M., Doubrawa, P., Herges, T., Martínez-Tossas, L. A., Maniaci, D. C., and Moriarty, P. Evaluation of Wind Speed Retrieval from Continuous-Wave Lidar Measurements of a Wind Turbine Wake Using Virtual Lidar Techniques. United States: N. p., 2019. Web. doi:10.1088/1742-6596/1256/1/012008.
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Debnath, M., Doubrawa, P., Herges, T., Martínez-Tossas, L. A., Maniaci, D. C., & Moriarty, P. Evaluation of Wind Speed Retrieval from Continuous-Wave Lidar Measurements of a Wind Turbine Wake Using Virtual Lidar Techniques. United States. https://doi.org/10.1088/1742-6596/1256/1/012008
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Debnath, M., Doubrawa, P., Herges, T., Martínez-Tossas, L. A., Maniaci, D. C., and Moriarty, P. Mon . "Evaluation of Wind Speed Retrieval from Continuous-Wave Lidar Measurements of a Wind Turbine Wake Using Virtual Lidar Techniques". United States. https://doi.org/10.1088/1742-6596/1256/1/012008. https://www.osti.gov/servlets/purl/1562860.
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@article{osti_1562860,
title = {Evaluation of Wind Speed Retrieval from Continuous-Wave Lidar Measurements of a Wind Turbine Wake Using Virtual Lidar Techniques},
author = {Debnath, M. and Doubrawa, P. and Herges, T. and Martínez-Tossas, L. A. and Maniaci, D. C. and Moriarty, P.},
abstractNote = {Wind lidar technology is being extensively used in utility-scale wind turbine wake measurements because of its capabilities to capture the dominant structures of interest in the near-wake region. However, wind lidar devices provide only line-of-sight (LOS) measurements, and retrieval of horizontal wind speed from the LOS measurements of a single lidar in the wind turbine wake could introduce errors due to the assumptions used in the retrieval process. With that in mind, the goals of this paper are to estimate the errors associated with the retrieval processes and provide guidelines for best practices. To achieve these goals, virtual lidar samples are obtained with large-eddy simulations (LES) by sampling the flow field with a model of the Technical University of Denmark (DTU) SpinnerLidar. LES is chosen to simulate the neutral and unstable atmospheric boundary layer (ABL), a benchmark case of the International Energy Agency Wind Technology Collaboration Programme (IEA Wind TCP) Task 31. The precursor ABL simulation is validated with the reference data provided by the IEA Wind Task 31 benchmark case. The data from the precursor simulation is used to drive a simulation of a wind turbine submerged in the ABL. A weighting function is applied to the LES data to mimic the real DTU SpinnerLidar measurements. In the end, the virtual lidar data are processed and compared with the original LES data to estimate the errors. Results show that lidar captures mean ABL profile without any noticeable error, whereas turbulence intensity is underpredicted ~15% at hub height for the neutral case. Volume averaging by the lidar technology has a significant effect in the shear layer of a wind turbine wake.},
doi = {10.1088/1742-6596/1256/1/012008},
journal = {Journal of Physics. Conference Series},
number = ,
volume = 1256,
place = {United States},
year = {Mon Jul 01 00:00:00 EDT 2019},
month = {Mon Jul 01 00:00:00 EDT 2019}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1088/1742-6596/1256/1/012008
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Cited by: 4 works
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Figures / Tables:

Table 1 Table 1: Mesh for the simulations
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Works referenced in this record:

Modelling lidar volume-averaging and its significance to wind turbine wake measurements
journal, May 2017

Can Wind Lidars Measure Turbulence?
journal, July 2011

  • Sathe, A.; Mann, J.; Gottschall, J.
  • Journal of Atmospheric and Oceanic Technology, Vol. 28, Issue 7
  • DOI: 10.1175/JTECH-D-10-05004.1

Focusing of truncated Gaussian beams
journal, July 2003

A spinner-integrated wind lidar for enhanced wind turbine control: Spinner-integrated wind lidar for enhanced steering and control
journal, October 2012

  • Mikkelsen, T.; Angelou, N.; Hansen, K.
  • Wind Energy, Vol. 16, Issue 4
  • DOI: 10.1002/we.1564

A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics
journal, January 2012

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    Table 1 (p. 3)table
    Figure 1 (p. 4)figure
    Figure 2 (p. 4)figure
    Table 2 (p. 6)table
    Figure 3 (p. 7)figure
    Figure 4 (p. 7)figure
    Figure 5 (p. 8)figure
    Figure 6 (p. 8)figure
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    Figure 8 (p. 9)figure
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