DOI: https://doi.org/10.14710/ijred.2023.47905

An Effect of Wind Veer on Wind Turbine Performance

1Multidisciplinary Graduate School Program for Wind Energy, Jeju National University, 102 Jejudaehakro, Jeju, 63243, South Korea

2Department of Electrical and Energy Engineering, Jeju National University, 102 Jejudaehakro, Jeju, 63243, South Korea

Received: 27 Jul 2022; Revised: 22 Sep 2022; Accepted: 25 Oct 2022; Available online: 8 Nov 2022; Published: 1 Jan 2023.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2023 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

An investigation was performed to identify the wind veer impact on wind turbine power performance at a wind farm located on Jeju Island, South Korea. A 2 MW wind turbine was used as a test turbine. An 80 m-tall met mast was located 220 m away from the test wind turbine and a ground lidar was installed close to the met mast. The wind veer conditions were divided into four types: veering in upper and lower rotor (VV), veering in upper and backing in lower rotor (VB), backing in upper and lower rotor (BB) and backing in upper and veering in lower rotor (BV). The frequency of the four types was identified at the wind farm. The characteristics of wind veer was analysed in terms of diurnal variation and wind speed. In addition, the power curves of the four types were compared with that under no veer condition. Also, the power deviation coefficient (PDC) derived from the power outputs was calculated to identify the effect of the four types on the turbine power performance. As a result, the frequencies of the types, VV, VB, BB and BV were 62.7 %, 4.9 %, 9.2 % and 23.1 %, respectively. The PDCs for the types VV and BV were 3.0 % and 4.2 %, respectively, meaning a power gain while those for the types VB and BB were -2.9 % and -3.9 %, respectively, meaning a power loss.

Fulltext View|Download
Keywords: Wind data; Ground lidar; Wind veer; Power curve; Power performance
Funding: Jeju National University

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Experimental Evaluation of Thermohydraulic Performance of Tubular Solar Air Heater An Investigation into the Role of Tourism Growth, Conventional Energy Consumption and Real Income on Ecological Footprint Nexus in France Effects of Injection Strategies on Mixture Formation and Combustion in a Spark-Ignition Engine Fueled with Syngas-Biogas-Hydrogen The Various Designs of Storage Solar Collectors: A Review More recent articles
Most cited articles
Potency of Microalgae as Biodiesel Source in Indonesia Influence of the Determination Methods of K and C Parameters on the Ability of Weibull Distribution to Suitably Estimate Wind Potential and Electric Energy Potential of Wind Energy in Albania and Kosovo: Equity Payback and GHG Reduction of Wind Turbine Installation Batch and Fed-Batch Fermentation System on Ethanol Production from Whey using Kluyveromyces marxianus Microgrids for rural schools: An energy-education accord to curb societal challenges for sustainable rural developments More cited articles
  1. Abkar, M., Sørensen, J.N., Porté-Agel, F., (2018). An analytical model for the effect of vertical wind veer on wind turbine wakes. Energies 11, 1838. https://doi.org/10.3390/en11071838
  2. Ansorge, C., Wurps, H., (2022). Wind veer and wind speed in turbulent Ekman flow. Copernicus Meetings. https://doi.org/10.1088/1742-6596/1256/1/012026
  3. Bahamonde, M.I., Litrán, S.P., (2019). Study of the energy production of a wind turbine in the open sea considering the continuous variations of the atmospheric stability and the sea surface roughness. Renewable Energy 135, 163–175. https://doi.org/10.1016/j.renene.2018.11.075
  4. Bodini, N., Lundquist, J.K., Kirincich, A., (2019). US East Coast lidar measurements show offshore wind turbines will encounter very low atmospheric turbulence. Geophysical Research Letters 46, 5582–5591. https://doi.org/10.1029/2019GL082636
  5. Brugger, P., Debnath, M., Scholbrock, A., Fleming, P., Moriarty, P., Simley, E., Jager, D., Roadman, J., Murphy, M., Zong, H., (2020). Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models. Wind Energy Science 5, 1253–1272. https://doi.org/10.5194/wes-5-1253-2020
  6. Brugger, P., Fuertes, F.C., Vahidzadeh, M., Markfort, C.D., Porté-Agel, F., (2019). Characterization of wind turbine wakes with Nacelle-Mounted Doppler LiDARs and model validation in the presence of wind veer. Remote Sensing 11, 2247. https://doi.org/10.3390/rs11192247
  7. Churchfield, M.J., Sirnivas, S., (2018). On the effects of wind turbine wake skew caused by wind veer, in: 2018 Wind Energy Symposium. p. 755. https://doi.org/10.2514/6.2018-0755
  8. Englberger, A., Lundquist, J.K., (2020). How does inflow veer affect the veer of a wind-turbine wake?, Journal of Physics: Conference Series. IOP Publishing, p. 12068. https://doi.org/10.1088/1742-6596/1452/1/012068
  9. Englberger, A., Lundquist, J.K., Dörnbrack, A., (2020). Should wind turbines rotate in the opposite direction? Wind Energy Science, 1–20. https://doi.org/10.5194/wes-2019-105
  10. Eriksson, O., Breton, S.-P., Nilsson, K., Ivanell, S., (2019). Impact of wind veer and the Coriolis force for an idealized farm to farm interaction case. Applied Sciences 9, 922. https://doi.org/10.3390/app9050922
  11. Gadde, S.N., Stevens, R.J.A.M., (2019). Effect of Coriolis force on a wind farm wake, in: Journal of Physics: Conference Series. IOP Publishing, p. 12026. https://doi.org/10.1088/1742-6596/1256/1/012026
  12. Gao, L., Li, B., Hong, J., (2021). Effect of wind veer on wind turbine power generation. Physics of Fluids 33, 15101. https://doi.org/10.1063/5.0033826
  13. Howland, M., Dabiri, J., (2020). Influence of atmospheric boundary layer wind speed and direction shear on utility-scale yaw misaligned turbines, in: APS Division of Fluid Dynamics Meeting Abstracts. pp. G03-004. https://doi.org/10.1063/5.0023746
  14. International Electrotechnical Commission, (2022a). Wind energy generation systems Part 12-5: Power performance – Assessment of obstacles and terrain, International Electrotechnical Commission
  15. International Electrotechnical Commission, (2022b). Wind energy generation systems Part 12-1: Power performance measurements of electricity producing wind turbines, International Electrotechnical Commission
  16. Kawabata, H., Kogaki, T., (2020). Lidar-assisted yaw control for wind turbines using a 9-beam nacelle lidar demonstrator, in: Journal of Physics: Conference Series. IOP Publishing, p. 12056. https://doi.org/10.1088/1742-6596/1452/1/012056
  17. Leosphere, (2014). Windcube V2 liDAR Remote Sensor User Manual
  18. Lundquist, J.K., (2022). Wind Shear and Wind Veer Effects on Wind Turbines, in: Handbook of Wind Energy Aerodynamics. Springer, pp. 1–22. https://doi.org/10.1007/978-3-030-05455-7_44-1
  19. Murphy, P., Lundquist, J.K., Fleming, P., (2020). How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine. Wind Energy Science 5, 1169–1190. https://doi.org/10.5194/wes-5-1169-2020
  20. Narasimhan, G., Gayme, D., Meneveau, C., (2021). Effect of veer on a yawed wind turbine wake in neutral and stable atmospheric boundary layer, in: APS Division of Fluid Dynamics Meeting Abstracts. pp. H03-007
  21. Sanchez Gomez, M., Lundquist, J.K., (2020a). The effect of wind direction shear on turbine performance in a wind farm in central Iowa. Wind Energy Science 5, 125–139. https://doi.org/10.5194/wes-5-125-2020
  22. Sanchez Gomez, M., Lundquist, J.K., (2020b). The Effects of Wind Veer During the Morning and Evening Transitions. Journal of Physics: Conference Series. IOP Publishing, p. 12075. https://doi.org/10.1088/1742-6596/1452/1/012075
  23. Sereema, (2020). Wind Turbine Yaw Misalignment: (R)Evolution
  24. Shin, D., Ko, K., (2019). Application of the nacelle transfer function by a nacelle-mounted light detection and ranging system to wind turbine power performance measurement. Energies 12, 1087. https://doi.org/10.3390/en12061087
  25. Shu, Z., Li, Q., He, Y., Chan, P.W., (2020a). Investigation of marine wind veer characteristics using wind lidar measurements. Atmosphere 11, 1178. https://doi.org/10.3390/atmos11111178
  26. Shu, Z., Li, Q.S., Chan, P.W., He, Y.C., (2020b). Seasonal and diurnal variation of marine wind characteristics based on lidar measurements. Meteorological Applications 27, e1918. https://doi.org/10.1002/met.1918
  27. Wagner, R., Courtney, M., Larsen, T.J., Paulsen, U.S., (2010). Simulation of shear and turbulence impact on wind turbine performance. Danmarks Tekniske Universitet, Risø Nationallaboratoriet for Bæredygtig Energi

Last update:

  1. Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

    Thanh Tuan Le, Prabhu Paramasivam, Elvis Adril, Van Quy Nguyen, Minh Xuan Le, Minh Thai Duong, Huu Cuong Le, Anh Quan Nguyen. International Journal of Renewable Energy Development, 13 (4), 2024. doi: 10.61435/ijred.2024.60387

  2. Investigation of wind veer characteristics on complex terrain using ground-based lidar

    Undarmaa Tumenbayar, Kyungnam Ko. International Journal of Renewable Energy Development, 13 (1), 2024. doi: 10.14710/ijred.2023.56352

Last update: 2024-12-26 21:01:14

No citation recorded.