skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Wind turbine wake characterization from temporally disjunct 3-D measurements

Abstract

Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes to probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. Lastly, we find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales,more » while density is more important when seeking to characterize the velocity deficit distribution.« less

Authors:
 [1];  [1];  [2];  [1];  [3]
  1. Cornell Univ., Ithaca, NY (United States)
  2. SpurrEnergy Ltd., Vancouver, BC (Canada)
  3. National Renewable Energy Lab. (NREL), Golden, CO (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)
OSTI Identifier:
1339506
Report Number(s):
NREL/JA-5000-67719
Journal ID: ISSN 2072-4292
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Accepted Manuscript
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Volume: 8; Journal Issue: 11; Journal ID: ISSN 2072-4292
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; wind energy; turbine; wakes; LiDAR

Citation Formats

Doubrawa, Paula, Barthelmie, Rebecca J., Wang, Hui, Pryor, S. C., and Churchfield, Matthew. Wind turbine wake characterization from temporally disjunct 3-D measurements. United States: N. p., 2016. Web. doi:10.3390/rs8110939.
Doubrawa, Paula, Barthelmie, Rebecca J., Wang, Hui, Pryor, S. C., & Churchfield, Matthew. Wind turbine wake characterization from temporally disjunct 3-D measurements. United States. doi:10.3390/rs8110939.
Doubrawa, Paula, Barthelmie, Rebecca J., Wang, Hui, Pryor, S. C., and Churchfield, Matthew. Thu . "Wind turbine wake characterization from temporally disjunct 3-D measurements". United States. doi:10.3390/rs8110939. https://www.osti.gov/servlets/purl/1339506.
@article{osti_1339506,
title = {Wind turbine wake characterization from temporally disjunct 3-D measurements},
author = {Doubrawa, Paula and Barthelmie, Rebecca J. and Wang, Hui and Pryor, S. C. and Churchfield, Matthew},
abstractNote = {Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes to probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. Lastly, we find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution.},
doi = {10.3390/rs8110939},
journal = {Remote Sensing},
number = 11,
volume = 8,
place = {United States},
year = {2016},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Save / Share:

Works referenced in this record:

Field Measurements of Wind Turbine Wakes with Lidars
journal, February 2013

  • Iungo, Giacomo Valerio; Wu, Yu-Ting; Porté-Agel, Fernando
  • Journal of Atmospheric and Oceanic Technology, Vol. 30, Issue 2
  • DOI: 10.1175/JTECH-D-12-00051.1

On the application of the Jensen wake model using a turbulence‐dependent wake decay coefficient: the Sexbierum case
journal, May 2015

  • Peña, Alfredo; Réthoré, Pierre‐Elouan; Laan, M. Paul
  • Wind Energy, Vol. 19, Issue 4
  • DOI: 10.1002/we.1863

Light detection and ranging measurements of wake dynamics part I: one-dimensional scanning
journal, January 2010

  • Bingöl, Ferhat; Mann, Jakob; Larsen, Gunner C.
  • Wind Energy, Vol. 13, Issue 1, p. 51-61
  • DOI: 10.1002/we.352

Variations of the Wake Height over the Bolund Escarpment Measured by a Scanning Lidar
journal, November 2015


Simulation of the inherent turbulence and wake interaction inside an infinitely long row of wind turbines
journal, April 2013

  • Andersen, Søren Juhl; Sørensen, Jens Nørkær; Mikkelsen, Robert
  • Journal of Turbulence, Vol. 14, Issue 4
  • DOI: 10.1080/14685248.2013.796085

Simulation of Coherent Doppler Lidar Performance in the Weak-Signal Regime
journal, June 1996


Effects of an escarpment on flow parameters of relevance to wind turbines: Flow over an escarpment at turbine relevant heights
journal, March 2016

  • Barthelmie, R. J.; Wang, H.; Doubrawa, P.
  • Wind Energy, Vol. 19, Issue 12
  • DOI: 10.1002/we.1980

Defining wake characteristics from scanning and vertical full- scale lidar measurements
journal, September 2016


Coherent Doppler lidar for wind farm characterization: Coherent Doppler lidar for wind farm characterization
journal, January 2012

  • Krishnamurthy, R.; Choukulkar, A.; Calhoun, R.
  • Wind Energy, Vol. 16, Issue 2
  • DOI: 10.1002/we.539

Wake Measurements of a Multi-MW Wind Turbine with Coherent Long-Range Pulsed Doppler Wind Lidar
journal, September 2010

  • Käsler, Yvonne; Rahm, Stephan; Simmet, Rudolf
  • Journal of Atmospheric and Oceanic Technology, Vol. 27, Issue 9, p. 1529-1532
  • DOI: 10.1175/2010JTECHA1483.1

Techniques of Wind Vector Estimation from Data Measured with a Scanning Coherent Doppler Lidar
journal, February 2003


Continuous-wave bistatic laser Doppler wind sensor
journal, January 2001

  • Harris, Michael; Constant, Graham; Ward, Carol
  • Applied Optics, Vol. 40, Issue 9
  • DOI: 10.1364/AO.40.001501

Lidar arc scan uncertainty reduction through scanning geometry optimization
journal, January 2016

  • Wang, Hui; Barthelmie, Rebecca J.; Pryor, Sara C.
  • Atmospheric Measurement Techniques, Vol. 9, Issue 4
  • DOI: 10.5194/amt-9-1653-2016

Investigating wind turbine impacts on near-wake flow using profiling lidar data and large-eddy simulations with an actuator disk model
journal, July 2015

  • Mirocha, Jeffrey D.; Rajewski, Daniel A.; Marjanovic, Nikola
  • Journal of Renewable and Sustainable Energy, Vol. 7, Issue 4
  • DOI: 10.1063/1.4928873

3D Wind and Turbulence Characteristics of the Atmospheric Boundary Layer
journal, May 2014

  • Barthelmie, R. J.; Crippa, P.; Wang, H.
  • Bulletin of the American Meteorological Society, Vol. 95, Issue 5
  • DOI: 10.1175/BAMS-D-12-00111.1

Lidar Investigation of Atmosphere Effect on a Wind Turbine Wake
journal, November 2013

  • Smalikho, I. N.; Banakh, V. A.; Pichugina, Y. L.
  • Journal of Atmospheric and Oceanic Technology, Vol. 30, Issue 11
  • DOI: 10.1175/JTECH-D-12-00108.1

Quantifying Wind Turbine Wake Characteristics from Scanning Remote Sensor Data
journal, April 2014

  • Aitken, Matthew L.; Banta, Robert M.; Pichugina, Yelena L.
  • Journal of Atmospheric and Oceanic Technology, Vol. 31, Issue 4
  • DOI: 10.1175/JTECH-D-13-00104.1

Characterisation of Single Wind Turbine Wakes with Static and Scanning WINTWEX-W LiDAR Data
journal, January 2015


Single-particle laser Doppler anemometry at 155 µm
journal, January 2001

  • Harris, Michael; Pearson, Guy N.; Ridley, Kevin D.
  • Applied Optics, Vol. 40, Issue 6
  • DOI: 10.1364/AO.40.000969

Light detection and ranging measurements of wake dynamics. Part II: two-dimensional scanning
journal, January 2011

  • Trujillo, Juan-José; Bingöl, Ferhat; Larsen, Gunner C.
  • Wind Energy, Vol. 14, Issue 1
  • DOI: 10.1002/we.402

Lidar-based Research and Innovation at DTU Wind Energy – a Review
journal, June 2014


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


Measurements of Boundary Layer Profiles in an Urban Environment
journal, June 2006

  • Frehlich, Rod; Meillier, Yannick; Jensen, Michael L.
  • Journal of Applied Meteorology and Climatology, Vol. 45, Issue 6
  • DOI: 10.1175/JAM2368.1

Utility-Scale Wind Turbine Wake Characterization Using Nacelle-Based Long-Range Scanning Lidar
journal, July 2014

  • Aitken, Matthew L.; Lundquist, Julie K.
  • Journal of Atmospheric and Oceanic Technology, Vol. 31, Issue 7
  • DOI: 10.1175/JTECH-D-13-00218.1

Numerical Modeling of Wind Turbine Wakes
journal, May 2002

  • So̸rensen, Jens No̸rkær; Shen, Wen Zhong
  • Journal of Fluids Engineering, Vol. 124, Issue 2
  • DOI: 10.1115/1.1471361

Hub Height Ocean Winds over the North Sea Observed by the NORSEWInD Lidar Array: Measuring Techniques, Quality Control and Data Management
journal, September 2013

  • Hasager, Charlotte; Stein, Detlef; Courtney, Michael
  • Remote Sensing, Vol. 5, Issue 9
  • DOI: 10.3390/rs5094280

A Lagrangian dynamic subgrid-scale model of turbulence
journal, July 1996

  • Meneveau, Charles; Lund, Thomas S.; Cabot, William H.
  • Journal of Fluid Mechanics, Vol. 319, Issue -1
  • DOI: 10.1017/S0022112096007379

Meteorological Controls on Wind Turbine Wakes
journal, April 2013

  • Barthelmie, Rebecca J.; Hansen, Kurt S.; Pryor, Sara C.
  • Proceedings of the IEEE, Vol. 101, Issue 4
  • DOI: 10.1109/JPROC.2012.2204029

Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics
journal, January 2015

  • Lundquist, J. K.; Churchfield, M. J.; Lee, S.
  • Atmospheric Measurement Techniques, Vol. 8, Issue 2
  • DOI: 10.5194/amt-8-907-2015

Coalescing Wind Turbine Wakes
journal, June 2015


    Works referencing / citing this record:

    Wind turbine wake characterization in complex terrain via integrated Doppler lidar data from the Perdigão experiment
    journal, June 2018


    Wake Management in Wind Farms: An Adaptive Control Approach
    journal, April 2019

    • Dhiman, Harsh; Deb, Dipankar; Muresan, Vlad
    • Energies, Vol. 12, Issue 7
    • DOI: 10.3390/en12071247

    IEA Wind Task 32: Wind Lidar Identifying and Mitigating Barriers to the Adoption of Wind Lidar
    journal, March 2018

    • Clifton, Andrew; Clive, Peter; Gottschall, Julia
    • Remote Sensing, Vol. 10, Issue 3
    • DOI: 10.3390/rs10030406

    Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation
    journal, April 2018

    • Carbajo Fuertes, Fernando; Markfort, Corey; Porté-Agel, Fernando
    • Remote Sensing, Vol. 10, Issue 5
    • DOI: 10.3390/rs10050668

    Using a Virtual Lidar Approach to Assess the Accuracy of the Volumetric Reconstruction of a Wind Turbine Wake
    journal, May 2018

    • Fuertes, Fernando; Porté-Agel, Fernando
    • Remote Sensing, Vol. 10, Issue 5
    • DOI: 10.3390/rs10050721

    Editorial for the Special Issue “Remote Sensing of Atmospheric Conditions for Wind Energy Applications”
    journal, April 2019

    • Hasager, Charlotte; Sjöholm, Mikael
    • Remote Sensing, Vol. 11, Issue 7
    • DOI: 10.3390/rs11070781

    Automated wind turbine wake characterization in complex terrain
    journal, January 2019

    • Barthelmie, Rebecca J.; Pryor, Sara C.
    • Atmospheric Measurement Techniques, Vol. 12, Issue 6
    • DOI: 10.5194/amt-12-3463-2019