• 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, 347355, doi:10.1175/JTECH-D-11-00033.1.

    • Search Google Scholar
    • Export Citation
  • Alfredsson, P.-H., and Dahlberg J.-Å. , 1981: Measurements of wake interaction effects on the power output from small wind turbine models. Aeronautical Research Institute of Sweden Tech. Note FFA HU-2189, 58 pp.

  • Ammara, I., Leclerc C. , and Masson C. , 2002: A viscous three-dimensional differential/actuator-disk method for the aerodynamic analysis of wind farms. J. Sol. Energy Eng.,124, 345356, doi:10.1115/1.1510870.

  • Aster, R. C., Borchers B. , and Thurber C. H. , 2013: Parameter Estimation and Inverse Problems. 2nd ed. Elsevier, 365 pp.

  • Baker, R. W., and Walker S. N. , 1984: Wake measurements behind a large horizontal axis wind turbine generator. Sol. Energy, 33, 512, doi:10.1016/0038-092X(84)90110-5.

    • Search Google Scholar
    • Export Citation
  • Banta, R. M., Olivier L. D. , Neff W. D. , Levinson D. H. , and Ruffieux D. , 1995: Influence of canyon-induced flows on flow and dispersion over adjacent plains. Theor. Appl. Climatol., 52, 2742, doi:10.1007/BF00865505.

    • Search Google Scholar
    • Export Citation
  • Banta, R. M., Olivier L. D. , Gudiksen P. H. , and Lange R. , 1996: Implications of small-scale flow features to modeling dispersion over complex terrain. J. Appl. Meteor., 35, 330342, doi:10.1175/1520-0450(1996)035<0330:IOSSFF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Banta, R. M., Newsom R. K. , Lundquist J. K. , Pichugina Y. L. , Coulter R. L. , and Mahrt L. , 2002: Nocturnal low-level jet characteristics over Kansas during CASES-99. Bound.-Layer Meteor., 105, 221252, doi:10.1023/A:1019992330866.

    • Search Google Scholar
    • Export Citation
  • Barthelmie, R. J., Folkerts L. , Ormel F. T. , Sanderhoff P. , Eecen P. J. , Stobbe O. , and Nielsen N. M. , 2003: Offshore wind turbine wakes measured by sodar. J. Atmos. Oceanic Technol., 20, 466477, doi:10.1175/1520-0426(2003)20<466:OWTWMB>2.0.CO;2.

    • 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, 888901, doi:10.1175/JTECH1886.1.

    • Search Google Scholar
    • Export Citation
  • Barthelmie, R. J., Frandsen S. T. , Nielsen N. M. , Pryor S. C. , Rethore P.-E. , and Jørgensen H. E. , 2007: Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm. Wind Energy, 10, 217228.

    • Search Google Scholar
    • Export Citation
  • Barthelmie, R. J., and Coauthors, 2010: Quantifying the impact of wind turbine wakes on power output at offshore wind farms. J. Atmos. Oceanic Technol., 27, 13021317, doi:10.1175/2010JTECHA1398.1.

    • 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, 5161, doi:10.1002/we.352.

    • Search Google Scholar
    • Export Citation
  • Box, G. E. P., Hunter J. S. , and Hunter W. G. , 2005: Statistics for Experimenters. Wiley-Interscience, 664 pp.

  • Cariou, J.-P., 2011: Pulsed lidars. Remote sensing for wind energy, A. Pena and C. B. Hasager, Eds., Risø Rep. Risø-I-3184(EN), 65–81.

  • Churchfield, M. J., Lee S. , Michalakes J. , and Moriarty P. J. , 2012: A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics. J. Turbul., 13, 132, doi:10.1080/14685248.2012.668191.

    • Search Google Scholar
    • Export Citation
  • Clifton, A., and Lundquist J. K. , 2012: Data clustering reveals climate impacts on local wind phenomena. J. Appl. Meteor. Climatol., 51, 15471557, doi:10.1175/JAMC-D-11-0227.1.

    • Search Google Scholar
    • Export Citation
  • Clive, P. J. M., Dinwoodie I. , and Quail F. , 2011: Direct measurement of wind turbine wakes using remote sensing. Proc. EWEA 2011, Brussels, Belgium, European Wind Energy Association, PO.181. [Available online at http://proceedings.ewea.org/annual2011/allfiles2/1502_EWEA2011presentation.pdf.]

  • Crespo, A., Hernandez J. , Fraga E. , and Andreu C. , 1988: Experimental validation of the UPM computer code to calculate wind turbine wakes and comparison with other models. J. Wind Eng. Ind. Aerodyn., 27, 7788, doi:10.1016/0167-6105(88)90025-6.

    • Search Google Scholar
    • Export Citation
  • Elliott, D. L., and Barnard J. C. , 1990: Observations of wind turbine wakes and surface roughness effects on wind flow variability. Sol. Energy, 45, 265283, doi:10.1016/0038-092X(90)90012-2.

    • Search Google Scholar
    • Export Citation
  • Elliott, D. L., Schwartz M. , and Scott G. , 2009: Wind shear and turbulence profiles at elevated heights: Great Lakes and Midwest sites. AWEA WindPower 2009 Conf., Chicago, IL, American Wind Energy Association, National Renewable Energy Laboratory Rep. PO-500-45455. [Available online at http://www.nrel.gov/docs/fy09osti/45455.pdf.]

  • Emeis, S., 2013: Wind Energy Meteorology. Springer, 196 pp.

  • Fitch, A. C., Olson J. B. , Lundquist J. K. , Dudhia J. , Gupta A. K. , Michalakes J. , and Barstad I. , 2012: Local and mesoscale impacts of wind farms as parameterized in a mesoscale NWP model. Mon. Wea. Rev., 140, 30173038, doi:10.1175/MWR-D-11-00352.1.

    • Search Google Scholar
    • Export Citation
  • Frandsen, S., Barthelmie R. , Pryor S. , Rathmann O. , Larsen S. , Højstrup J. , and Thøgersen M. , 2006: Analytical modelling of wind speed deficit in large offshore wind farms. Wind Energy, 9, 3953, doi:10.1002/we.189.

    • Search Google Scholar
    • Export Citation
  • Frehlich, R., Meillier Y. , Jensen M. L. , and Balsley B. , 2006: Measurements of boundary layer profiles in an urban environment. J. Appl. Meteor. Climatol., 45, 821837, doi:10.1175/JAM2368.1.

    • Search Google Scholar
    • Export Citation
  • Grund, C. J., Banta R. M. , George J. L. , Howell J. N. , Post M. J. , Richter R. A. , and Weickmann A. M. , 2001: High-resolution Doppler lidar for boundary layer and cloud research. J. Atmos. Oceanic Technol., 18, 376393, doi:10.1175/1520-0426(2001)018<0376:HRDLFB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Haines, R. S., Milborrow D. J. , Page D. I. , Scott A. D. , Stevenson W. G. , and Taylor G. J. , 1986: Wake interactions between the Holden HWP-300 and the WEG MS-1 wind turbine generators on Orkney, U.K. Proceedings of the European Wind Energy Association Conference and Exhibition (EWEC’86), W. Palz and E. Sesto, Eds., European Wind Energy Association, 435455.

  • Hansen, K. S., Barthelmie R. J. , Jensen L. E. , and Sommer A. , 2012: The impact of turbulence intensity and atmospheric stability on power deficits due to wind turbine wakes at Horns Rev wind farm. Wind Energy, 15, 183196, doi:10.1002/we.512.

    • Search Google Scholar
    • Export Citation
  • Helmis, C. G., Papadopoulos K. H. , Asimakopoulos D. N. , Papageorgas P. G. , and Soilemes A. T. , 1995: An experimental study of the near-wake structure of a wind turbine operating over complex terrain. Sol. Energy, 54, 413428, doi:10.1016/0038-092X(95)00009-G.

    • Search Google Scholar
    • Export Citation
  • 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, 3946.

    • 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, 765771, doi:10.1175/JTECH-D-12-00039.1.

    • Search Google Scholar
    • Export Citation
  • Högström, U., Asimakopoulos D. N. , Kambezidis H. , Helmis C. G. , and Smedman A. , 1988: A field study of the wake behind a 2 MW wind turbine. Atmos. Environ., 22, 803820, doi:10.1016/0004-6981(88)90020-0.

    • Search Google Scholar
    • Export Citation
  • IEC, 2005: Wind turbines—Part 12-1: Power performance measurements of electricity producing wind turbines. IEC 61400-12-1 International Standard, 92 pp.

  • Iungo, G. V., Wu Y.-T. , and Porté-Agel F. , 2013: Field measurements of wind turbine wakes with lidars. J. Atmos. Oceanic Technol., 30, 274287, doi:10.1175/JTECH-D-12-00051.1.

    • Search Google Scholar
    • Export Citation
  • Johansson, P. B., George W. K. , and Gourlay M. J. , 2003: Equilibrium similarity, effects of initial conditions and local Reynolds number on the axisymmetric wake. Phys. Fluids, 15, 603617, doi:10.1063/1.1536976.

    • Search Google Scholar
    • Export Citation
  • Johnson, W., and Kelley N. , 2000: Design specifications for the development of the initial validation software (version 3.0) for processing of NWTC 80-meter meteorological tower data. National Renewable Energy Laboratory Tech. Rep. NREL/TP-500-27104, 92 pp. [Available online at http://www.nrel.gov/docs/fy00osti/27104.pdf.]

  • Justus, C. G., Hargraves W. R. , Mikhail A. , and Graber D. , 1978: Methods for estimating wind speed frequency distributions. J. Appl. Meteor., 17, 350353, doi:10.1175/1520-0450(1978)017<0350:MFEWSF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kambezidis, H. D., Asimakopoulos D. N. , and Helmis C. G. , 1990: Wake measurements behind a horizontal-axis 50 kW wind turbine. Solar Wind Technol., 7, 177184, doi:10.1016/0741-983X(90)90085-G.

    • Search Google Scholar
    • Export Citation
  • Käsler, Y., Rahm S. , Simmet R. , and Kühn M. , 2010: Wake measurements of a multi-MW wind turbine with coherent long-range pulsed Doppler wind lidar. J. Atmos. Oceanic Technol., 27, 15291532, 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, 107120, doi:10.1002/we.49.

  • Magnusson, M., 1999: Near-wake behaviour of wind turbines. J. Wind Eng. Ind. Aerodyn., 80, 147167, doi:10.1016/S0167-6105(98)00125-1.

    • Search Google Scholar
    • Export Citation
  • Magnusson, M., and Smedman A. S. , 1994: Influence of atmospheric stability on wind turbine wakes. Wind Eng., 18, 139151.

  • 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, 94102, doi:10.1016/j.enconman.2012.11.025.

    • Search Google Scholar
    • Export Citation
  • Meyers, J., and Meneveau C. , 2012: Optimal turbine spacing in fully developed wind farm boundary layers. Wind Energy, 15, 305317, doi:10.1002/we.469.

    • Search Google Scholar
    • Export Citation
  • Newsom, R. K., and Banta R. M. , 2003: Shear-flow instability in the stable nocturnal boundary layer as observed by Doppler lidar during CASES-99. J. Atmos. Sci., 60, 1633, doi:10.1175/1520-0469(2003)060<0016:SFIITS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Newsom, R. K., Ligon D. , Calhoun R. , Heap R. , Cregan E. , and Princevac M. , 2005: Retrieval of microscale wind and temperature fields from single- and dual-Doppler lidar data. J. Appl. Meteor., 44, 13241345, doi:10.1175/JAM2280.1.

    • Search Google Scholar
    • Export Citation
  • Pichugina, Y. L., Banta R. M. , Brewer W. A. , Sandberg S. P. , and Hardesty R. M. , 2012: Doppler lidar–based wind-profile measurement system for offshore wind-energy and other marine boundary layer applications. J. Appl. Meteor. Climatol., 51, 327349, doi:10.1175/JAMC-D-11-040.1.

    • Search Google Scholar
    • Export Citation
  • Porté-Agel, F., Wu Y.-T. , Lu H. , and Conzemius R. J. , 2011: Large-eddy simulation of atmospheric boundary layer flow through wind turbines and wind farms. J. Wind Eng. Ind. Aerodyn., 99, 154168, doi:10.1016/j.jweia.2011.01.011.

    • Search Google Scholar
    • Export Citation
  • Rajewski, D. A., and Coauthors, 2013: Crop Wind Energy Experiment (CWEX): Observations of surface-layer, boundary layer, and mesoscale interactions with a wind farm. Bull. Amer. Meteor. Soc., 94, 655672, doi:10.1175/BAMS-D-11-00240.1.

    • 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, 1627, 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, 25542570, doi:10.1175/JTECH-D-12-00108.1.

    • Search Google Scholar
    • Export Citation
  • Stull, R. B., 1988: An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, 666 pp.

  • Trujillo, J.-J., Bingöl F. , Larsen G. C. , Mann J. , and Kühn M. , 2011: Light detection and ranging measurements of wake dynamics. Part II: Two-dimensional scanning. Wind Energy, 14, 6175, doi:10.1002/we.402.

    • Search Google Scholar
    • Export Citation
  • Tucker, S. C., Brewer W. A. , Banta R. M. , Senff C. J. , Sandberg S. P. , Law D. C. , Weickmann A. M. , and Hardesty R. M. , 2009: Doppler lidar estimation of mixing height using turbulence, shear, and aerosol profiles. J. Atmos. Oceanic Technol., 26, 673688, doi:10.1175/2008JTECHA1157.1.

    • Search Google Scholar
    • Export Citation
  • Vermeer, L. J., Sørensen J. N. , and Crespo A. , 2003: Wind turbine wake aerodynamics. Prog. Aerosp. Sci., 39, 467510, doi:10.1016/S0376-0421(03)00078-2.

    • Search Google Scholar
    • Export Citation
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Quantifying Wind Turbine Wake Characteristics from Scanning Remote Sensor Data

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  • 1 Department of Physics, University of Colorado Boulder, Boulder, Colorado
  • | 2 NOAA/Earth System Research Laboratory, Boulder, Colorado
  • | 3 Cooperative Institute for Research in Environmental Sciences, and NOAA/Earth System Research Laboratory, Boulder, Colorado
  • | 4 Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, and National Renewable Energy Laboratory, Golden, Colorado
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Abstract

Because of the dense arrays at most wind farms, the region of disturbed flow downstream of an individual turbine leads to reduced power production and increased structural loading for its leeward counterparts. Currently, wind farm wake modeling, and hence turbine layout optimization, suffers from an unacceptable degree of uncertainty, largely because of a lack of adequate experimental data for model validation. Accordingly, nearly 100 h of wake measurements were collected with long-range Doppler lidar at the National Wind Technology Center at the National Renewable Energy Laboratory in the Turbine Wake and Inflow Characterization Study (TWICS). This study presents quantitative procedures for determining critical parameters from this extensive dataset—such as the velocity deficit, the size of the wake boundary, and the location of the wake centerline—and categorizes the results by ambient wind speed, turbulence, and atmospheric stability. Despite specific reference to lidar, the methodology is general and could be applied to extract wake characteristics from other remote sensor datasets, as well as computational simulation output.

The observations indicate an initial velocity deficit of 50%−60% immediately behind the turbine, which gradually declines to 15%−25% at a downwind distance x of 6.5 rotor diameters (D). The wake expands with downstream distance, albeit less so in the vertical direction due to the presence of the ground: initially the same size as the rotor, the extent of the wake grows to 2.7D (1.2D) in the horizontal (vertical) at x = 6.5D. Moreover, the vertical location of the wake center shifts upward with downstream distance because of the tilt of the rotor.

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

Because of the dense arrays at most wind farms, the region of disturbed flow downstream of an individual turbine leads to reduced power production and increased structural loading for its leeward counterparts. Currently, wind farm wake modeling, and hence turbine layout optimization, suffers from an unacceptable degree of uncertainty, largely because of a lack of adequate experimental data for model validation. Accordingly, nearly 100 h of wake measurements were collected with long-range Doppler lidar at the National Wind Technology Center at the National Renewable Energy Laboratory in the Turbine Wake and Inflow Characterization Study (TWICS). This study presents quantitative procedures for determining critical parameters from this extensive dataset—such as the velocity deficit, the size of the wake boundary, and the location of the wake centerline—and categorizes the results by ambient wind speed, turbulence, and atmospheric stability. Despite specific reference to lidar, the methodology is general and could be applied to extract wake characteristics from other remote sensor datasets, as well as computational simulation output.

The observations indicate an initial velocity deficit of 50%−60% immediately behind the turbine, which gradually declines to 15%−25% at a downwind distance x of 6.5 rotor diameters (D). The wake expands with downstream distance, albeit less so in the vertical direction due to the presence of the ground: initially the same size as the rotor, the extent of the wake grows to 2.7D (1.2D) in the horizontal (vertical) at x = 6.5D. Moreover, the vertical location of the wake center shifts upward with downstream distance because of the tilt of the rotor.

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