Advertisement

Journal of Mechanical Science and Technology

, Volume 33, Issue 4, pp 1663–1671 | Cite as

Comparison of wind turbine power curves using cup anemometer and pulsed doppler light detection and ranging systems

  • Dongheon Shin
  • Kyungnam KoEmail author
  • Minsang Kang
  • Donghun Ryu
  • Munjong Kang
  • Hyunsik Kim
Article
  • 8 Downloads

Abstract

To clarify the difference in the wind turbine power curves obtained by cup anemometer and light detection and ranging (LIDAR) measurements, an investigation was experimentally performed in the Haengwon wind farm on Jeju Island, South Korea. A LIDAR mounted on the nacelle of a 1.5-MW test wind turbine was used with a met mast and a ground LIDAR positioned at a distance of 2.5 times the rotor diameter from the turbine. The wind speed data obtained by each instrument were compared through linear regression analysis. The rotor equivalent wind speed (REWS) was derived from a cup anemometer and ground LIDAR measurements in accordance with the International Electrotechnical Commission (TEC) standard 61400-12-1, 2nd edition. The scatter plots were drawn using the wind data measured by each instrument and compared in terms of the standard deviation. The power curves drawn from the REWS and nacelle LIDAR measurements were compared with that from the cup anemometer measurements according to IEC 61400-12-1, 1st edition. To quantitatively identify the difference in the power curves, the relative error was calculated using the cup anemometer power curve as a reference. Consequently, the relative error for the power output in the bin interval of 0.5 m/s before the rated wind speed was high, whereas that after the rated was close to 0 %. The relative errors for the power curve from the REWS and the nacelle LIDAR measurements were −0.37 % and 3.01 % on average, respectively.

Keywords

Nacelle light detection and ranging (LIDAR) Power curve Power performance Rotor equivalent wind speed (REWS) Wind turbine 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    B. Cañadillas, A. Westerhellweg and T. Neumann, Testing the performance of a ground-based wind LiDAR system: One year intercomparison at the offshore platform FTNO1, DEWI Magazine, 38 (2011).Google Scholar
  2. [2]
    S. Lang and E. McKeogh, LIDAR and SODAR measurements of wind speed and direction in upland terrain for wind energy purposes, Remote Sensing, 3 (2011) 1871–1901.CrossRefGoogle Scholar
  3. [3]
    S. Bourgeois, Documentation and Results of the SODAR and LIDAR Measurements at the Maligrad Site in Bosnia and Herzegovina, Meteotest (2008).Google Scholar
  4. [4]
    H. G. Kim, C. W. Chyng, H. J. An and Y. M. Ji, Comparative validation of windcube LIDAR and remtech SODAR for wind resource assessment-Remote sensing campaign at Pohang Accelerator Laboratory, J. Korean Solar Energy Society, 31 (2) (2011)63–71.CrossRefGoogle Scholar
  5. [5]
    H. G. Kim and H. C. Ji, Uncertainty analysis on wind speed profile measurements of LiDAR by applying SODAR measurements as a virtual true value, J. Korean Solar Energy Society, 30 (4) (2010)79–85.Google Scholar
  6. [6]
    D. Y. Kim, T. W. Kim, G. J. Oh, J. C. Huh and K. N. Ko, A comparison of ground-based LiDAR and met mast wind measurements for wind resource assessment over various terrain conditions, J. Wind Eng. Ind Aerodyn., 158 (2016) 109–121.CrossRefGoogle Scholar
  7. [7]
    Z. R. Shu, Q. S. Li, Y. C. He and P. W. Chan, Observations of offshore wind characteristics by Doppler-LiDAR for wind energy applications, Appl Energy, 169 (2016) 150–163.CrossRefGoogle Scholar
  8. [8]
    Z. R. Shu, Q. S. Li and P. W. Chan, Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function, Appl Energy, 156 (2015) 362–373.CrossRefGoogle Scholar
  9. [9]
    S. Wan, L. Cheng and X. Sheng, Numerical analysis of the spatial distribution of equivalent wind speeds in large-scale wind turbines, J. Mech. Sci. Technol, 31 (2) (2017) 965–74.CrossRefGoogle Scholar
  10. [10]
    International Electrotechnical Commission, Wind Turbine Generator Systems Part 12-1: Power performance Measurements of Electricity Producing Wind Turbines, Second edition, IEC 61400-12-1 (2017).Google Scholar
  11. [11]
    M. Courtney, Calibrating Nacelle Lidars, DTU Wind Energy (2013).Google Scholar
  12. [12]
    A. Borraccino, M. Courtney and R. Wagner, Generic methodology for calibrating profiling nacelle lidars, DTU Wind Energy Report (2015).Google Scholar
  13. [13]
    R. Wagner, R. L. Rivera, I. Antoniou, S. Davoust, T. F. Pedersen, M. Courtney and B. Diznabi, Procedure for wind turbine power performance measurement with a two-beam nacelle LiDAR, DTU Wind Energy Report (2013).Google Scholar
  14. [14]
    R. Wagner and D. Samuel, Nacelle Lidar for power curve meas-urementAvedere campaign, DTU WindEnergy Report (2013).Google Scholar
  15. [15]
    R. Wagner, T. F. Pedersen, M. Courtney, J. Gottschall, I. Antoniou, R. Mailer, S. M. Pedersen, T. Velociter, M. Bardon and A. S. Mouritzen, Power performance measured using a nacelle Lidar, EWEA Annual Event (2011).Google Scholar
  16. [16]
    R. Wagner, A. Sathe, A. Mioullet and M. Courtney, Turbulence measurement with a two-beam nacelle lidar, EWEA Annual Event (2013).Google Scholar
  17. [17]
    S. Davoust, A. Jehu, M. Bouillet, M. Bardon and B. Vercherin, Assessment and Optimization of Lidar Measurement Availability for Wind Turbine Control, National Renewable Energy Laboratory (NREL) (2014).Google Scholar
  18. [18]
    P. A. Fleming, A. K. Scholbrock, A. Jehu, S. Davoust, E. Osier, A. D. Wright and A. Clifton, Field-test results using a nacelle-mounted lidar for improving wind turbine power capture by reducing yaw misalignment, Journal of Physics: Conference Series, 524 (1) (2014) 012002 IOP Publishing.Google Scholar
  19. [19]
    D. Schlipf, P. Fleming, F. Haizmann, A. Scholbrock, M. Hofsäß, A. Wright and P. W. Cheng, Field testing of feedforward collective pitch control on the CART2 using a nacelle-based lidar scanner, Journal of Physics: Conference Series, 555 (1) (2014) 012090. IOP Publishing.Google Scholar
  20. [20]
    D. H. Shin, K. N. Ko and M. S. Kang, Characteristics analysis and reliability verification of nacelle lidar measurements, J. Korean Solar Energy Society, 37 (5) (2017) 1–11.Google Scholar
  21. [21]
    International Electrotechnical Commission, Wind Turbine Generator Systems Part 12-1: Power Performance Measurements of Electricity Producing Wind Turbines, First Edition, IEC 61400-12-1 (2005).Google Scholar
  22. [22]
    A. Albers, H. Klug and D. Westermann, Power performance verification, 1999 European Wind Energy Conference, Nice, France (1999) 657–60.Google Scholar
  23. [23]
    B. Smith, H. Link, G. Randall and T. McCoy; Applicability of Nacelle Anemometer Measurements for Use in Turbine Power Performance Tests, National Renewable Energy Laboratory (2002).Google Scholar
  24. [24]
    H. Suzuki, J. Suzuki, Y. Fujita and A. Muto, Evaluation of Wind Turbine Power Curve with Nacelle Anemometer, Jpn Wind Energy Assoc. (2013) 228–31.Google Scholar
  25. [25]
    W. Hernandez, J. L. Lopez-Presa and J. L. Maldonado-Correa, Power performance verification of a wind farm using the Friedman’s test, Sensors, 16 (6) (2016) 816.CrossRefGoogle Scholar
  26. [26]
    H. S. Oh and B. S. Kim, Comparison and verification of the deviation between guaranteed and measured wind turbine power performance in complex terrain, Energy, 85 (2015) 23–9.CrossRefGoogle Scholar
  27. [27]
    A. Curvers and P. A. Van der Werff, OWEZ Wind Farm Efficiency, ECN (2008).Google Scholar
  28. [28]
    H. W. Kim, K. N. Ko and J. C. Huh, Wind turbine power performance testing using nacelle transfer function, J. Korean Solar Energy Society, 33 (4) (2013) 51–8.CrossRefGoogle Scholar
  29. [29]
    International Electrotechnical Commission, Wind Turbine Generator Systems Part 12-2: Power Performance of Electricity-producing Wind Turbines based on Nacelle Anemometry, First edition, IEC 61400-12-2 (2013).Google Scholar
  30. [30]
    D. H. Shin, H. W. Kim and K. N. Ko, Analysis of wind turbine degradation via the nacelle transfer function, J. Mech. Sci. Technol., 29 (9) (2015) 1–8.CrossRefGoogle Scholar
  31. [31]
    D. H. Shin and K. N. Ko, Comparative analysis of degradation rates for inland and seaside wind turbines in compliance with the International Electrotechnical Commission standard, Energy, 118 (2017) 1180–6.CrossRefGoogle Scholar
  32. [32]
  33. [33]
    Windcube V2 LiDAR Remote Sensor User Manual version 06, Leoshphere, France.Google Scholar
  34. [34]
    Wind Iris User Manual with Software 1.5.1, Avent Lidar Technology.Google Scholar
  35. [35]
    M. C. Brower, Wind Resource Assessment: A Practical Guide to Developing a Wind Project, Wiley (2012).CrossRefGoogle Scholar

Copyright information

© KSME & Springer 2019

Authors and Affiliations

  • Dongheon Shin
    • 1
  • Kyungnam Ko
    • 2
    Email author
  • Minsang Kang
    • 3
  • Donghun Ryu
    • 4
  • Munjong Kang
    • 5
  • Hyunsik Kim
    • 6
  1. 1.Multidisciplinary Graduate School Program for Wind EnergyJeju National UniversityJejuKorea
  2. 2.Faculty of Wind Energy Engineering, Graduate SchoolJeju National UniversityJejuKorea
  3. 3.Research & Development CenterJeju Energy CorporationJejuKorea
  4. 4.Industrial Standards DivisionKorea Testing LaboratoryGyeonggi-doKorea
  5. 5.Future Technology Research TeamKorean RegisterBusanKorea
  6. 6.Renewable Energy TeamVisionplus Co., LtdSeognam-si, Kyunggi-doKorea

Personalised recommendations