Utility-Scale Wind Turbine Wake Characterization Using Nacelle-Based Long-Range Scanning Lidar

Matthew L. Aitken Department of Physics, University of Colorado Boulder, Boulder, Colorado

Search for other papers by Matthew L. Aitken in
Current site
Google Scholar
PubMed
Close
and
Julie K. Lundquist Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, and National Renewable Energy Laboratory, Golden, Colorado

Search for other papers by Julie K. Lundquist in
Current site
Google Scholar
PubMed
Close
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

To facilitate the optimization of turbine spacing at modern wind farms, computational simulations of wake effects must be validated through comparison with full-scale field measurements of wakes from utility-scale turbines operating in the real atmosphere. Scanning remote sensors are particularly well suited for this objective, as they can sample wind fields over large areas at high temporal and spatial resolutions. Although ground-based systems are useful, the vantage point from the nacelle is favorable in that scans can more consistently transect the central part of the wake. To the best of the authors’ knowledge, the work described here represents the first analysis in the published literature of a utility-scale wind turbine wake using nacelle-based long-range scanning lidar.

The results presented are of a field experiment conducted in the fall of 2011 at a wind farm in the western United States, quantifying wake attributes such as the velocity deficit, centerline location, and wake width. Notable findings include a high average velocity deficit, decreasing from 60% at a downwind distance x of 1.8 rotor diameters (D) to 40% at x = 6D, resulting from a low average wind speed and therefore a high average turbine thrust coefficient. Moreover, the wake width was measured to expand from 1.5D at x = 1.8D to 2.5D at x = 6D. Both the wake growth rate and the amplitude of wake meandering were observed to be greater for high ambient turbulence intensity and daytime conditions as compared to low turbulence and nocturnal conditions.

Corresponding author address: Matthew Aitken, Department of Physics, University of Colorado Boulder, 390 UCB, Boulder, CO 80309-0390. E-mail: matthew.aitken@colorado.edu

Abstract

To facilitate the optimization of turbine spacing at modern wind farms, computational simulations of wake effects must be validated through comparison with full-scale field measurements of wakes from utility-scale turbines operating in the real atmosphere. Scanning remote sensors are particularly well suited for this objective, as they can sample wind fields over large areas at high temporal and spatial resolutions. Although ground-based systems are useful, the vantage point from the nacelle is favorable in that scans can more consistently transect the central part of the wake. To the best of the authors’ knowledge, the work described here represents the first analysis in the published literature of a utility-scale wind turbine wake using nacelle-based long-range scanning lidar.

The results presented are of a field experiment conducted in the fall of 2011 at a wind farm in the western United States, quantifying wake attributes such as the velocity deficit, centerline location, and wake width. Notable findings include a high average velocity deficit, decreasing from 60% at a downwind distance x of 1.8 rotor diameters (D) to 40% at x = 6D, resulting from a low average wind speed and therefore a high average turbine thrust coefficient. Moreover, the wake width was measured to expand from 1.5D at x = 1.8D to 2.5D at x = 6D. Both the wake growth rate and the amplitude of wake meandering were observed to be greater for high ambient turbulence intensity and daytime conditions as compared to low turbulence and nocturnal conditions.

Corresponding author address: Matthew Aitken, Department of Physics, University of Colorado Boulder, 390 UCB, Boulder, CO 80309-0390. E-mail: matthew.aitken@colorado.edu
Save
  • Aitken, M. L., Rhodes M. E. , and Lundquist J. K. , 2012: Performance of a wind-profiling lidar in the region of wind turbine rotor disks. J. Atmos. Oceanic Technol., 29, 347–355, doi:10.1175/JTECH-D-11-00033.1.

    • Search Google Scholar
    • Export Citation
  • Aitken, M. L., Banta R. M. , Pichugina Y. L. , and Lundquist J. K. , 2014: Quantifying wind turbine wake characteristics from scanning remote sensor data. J. Atmos. Oceanic Technol., 31, 765–787, doi:10.1175/JTECH-D-13-00104.1.

    • Search Google Scholar
    • Export Citation
  • Barthelmie, R. J., Folkerts L. , Larsen G. C. , Rados K. , Pryor S. C. , Frandsen S. T. , Lange B. , and Schepers G. , 2006: Comparison of wake model simulations with offshore wind turbine wake profiles measured by sodar. J. Atmos. Oceanic Technol., 23, 888–901, doi:10.1175/JTECH1886.1.

    • Search Google Scholar
    • Export Citation
  • Bingöl, F., Mann J. , and Larsen G. C. , 2010: Light detection and ranging measurements of wake dynamics part I: One-dimensional scanning. Wind Energy, 13, 51–61, doi:10.1002/we.352.

    • Search Google Scholar
    • Export Citation
  • Elliott, D., Schwartz M. , and Scott G. , 2009: Wind shear and turbulence profiles at elevated heights: Great Lakes and Midwest sites. NREL Rep. PO-500-45455, 1 pp.

  • España, G., Aubrun S. , Loyer S. , and Devinant P. , 2011: Spatial study of the wake meandering using modelled wind turbines in a wind tunnel. Wind Energy, 14, 923–937, doi:10.1002/we.515.

    • Search Google Scholar
    • Export Citation
  • Friedrich, K., Lundquist J. K. , Aitken M. , Kalina E. A. , and Marshall R. F. , 2012: Stability and turbulence in the atmospheric boundary layer: A comparison of remote sensing and tower observations. Geophys. Res. Lett., 39, L03801, doi:10.1029/2011GL050413.

    • Search Google Scholar
    • Export Citation
  • Fujii, T., and Fukuchi T. , Eds., 2005: Laser Remote Sensing. CRC Press, 912 pp.

  • Hirth, B. D., and Schroeder J. L. , 2013: Documenting wind speed and power deficits behind a utility-scale wind turbine. J. Appl. Meteor. Climatol., 52, 39–46, doi:10.1175/JAMC-D-12-0145.1.

    • Search Google Scholar
    • Export Citation
  • Hirth, B. D., Schroeder J. L. , Gunter W. S. , and Guynes J. G. , 2012: Measuring a utility-scale turbine wake using the TTUKa mobile research radars. J. Atmos. Oceanic Technol., 29, 765–771, doi:10.1175/JTECH-D-12-00039.1.

    • Search Google Scholar
    • Export Citation
  • Iungo, G. V., Wu Y.-T. , and PortĂ©-Agel F. , 2013: Field measurements of wind turbine wakes with lidars. J. Atmos. Oceanic Technol., 30, 274–287, doi:10.1175/JTECH-D-12-00051.1.

    • Search Google Scholar
    • Export Citation
  • Justus, C. G., Hargraves W. R. , Mikhail A. , and Graber D. , 1978: Methods for estimating wind speed frequency distributions. J. Appl. Meteor., 17, 350–353, doi:10.1175/1520-0450(1978)017<0350:MFEWSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Käsler, Y., Rahm S. , Simmet R. , and KĂĽhn M. , 2010: Wake measurements of a multi-MW wind turbine with coherent long-range pulsed Doppler wind lidar. J. Atmos. Oceanic Technol., 27, 1529–1532, doi:10.1175/2010JTECHA1483.1.

    • Search Google Scholar
    • Export Citation
  • Kleinbaum, D. G., Kupper L. L. , Nizam A. , and Muller K. E. , 2007 : Applied Regression Analysis and Other Multivariable Methods. 4th ed. Duxbury Press, 928 pp.

  • Larsen, G. C., 2001: Offshore fatigue design turbulence. Wind Energy, 4, 107–120, doi:10.1002/we.49.

  • Magnusson, M., 1999: Near-wake behavior of wind turbines. J. Wind Eng. Ind. Aerodyn., 80, 147–167, doi:10.1016/S0167-6105(98)00125-1.

    • Search Google Scholar
    • Export Citation
  • Mahrt, L., 2011: Surface wind direction variability. J. Appl. Meteor. Climatol., 50, 144–152, doi:10.1175/2010JAMC2560.1.

  • Manwell, J. F., McGowan J. G. , and Rogers A. L. , 2010: Wind Energy Explained: Theory, Design, and Application. Wiley, 704 pp.

  • Masseran, N., Razali A. M. , Ibrahim K. , and Latif M. T. , 2013: Fitting a mixture of von Mises distributions in order to model data on wind direction in Peninsular Malaysia. Energy Convers. Manage., 72, 94–102, doi:10.1016/j.enconman.2012.11.025.

    • Search Google Scholar
    • Export Citation
  • Rye, B. J., and Hardesty R. M. , 1993: Discrete spectral peak estimation in incoherent backscatter heterodyne lidar. I: Spectral accumulation and the Cramer-Rao lower bound. IEEE Trans. Geosci. Remote Sens., 31, 16–27, doi:10.1109/36.210440.

    • Search Google Scholar
    • Export Citation
  • Smalikho, I. N., Banakh V. A. , Pichugina Y. L. , Brewer W. A. , Banta R. M. , Lundquist J. K. , and Kelley N. D. , 2013: Lidar investigation of atmosphere effect on a wind turbine wake. J. Atmos. Oceanic Technol., 30, 2554–2570, doi:10.1175/JTECH-D-12-00108.1.

    • Search Google Scholar
    • Export Citation
  • Trujillo, J.-J., Bingöl F. , Larsen G. C. , Mann J. , and KĂĽhn M. , 2011: Light detection and ranging measurements of wake dynamics part II: Two-dimensional scanning. Wind Energy, 14, 61–75, doi:10.1002/we.402.

    • Search Google Scholar
    • Export Citation
  • Werner, C., 2005: Doppler wind lidar. Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, C. Weitkamp, Ed., Springer, 325–354.

  • Wharton, S., and Lundquist J. K. , 2012: Assessing atmospheric stability and its impacts on rotor-disk wind characteristics at an onshore wind farm. Wind Energy, 15, 525–546, doi:10.1002/we.483.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1100 425 165
PDF Downloads 513 100 7