DOI: https://doi.org/10.14710/ijred.2023.56352

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

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: 12 Jul 2023; Revised: 6 Oct 2023; Accepted: 26 Oct 2023; Available online: 5 Nov 2023; Published: 1 Jan 2024.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2024 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
The wind direction shift with height significantly influences wind turbine performance, particularly in relation to terrain conditions. In this work, wind conditions at 12 measurement heights ranging from 40 m to 200 m using a ground lidar, Windcube V2, installed on a 16 m tall building were analysed to examine the characteristics of wind veer angles in complex terrain. The measurement campaign was carried out from January 1st to December 31st, 2022, in the southeastern part of South Korea. The terrain complexity around the ground lidar system was evaluated using the ruggedness index (RIX), whose result was 14.06 percent corresponding to complex terrain. The ground lidar measurements were compared with mesoscale data, EMD-WRF South Korea, for the data accuracy check. Wind veer frequencies and wind roses were derived to identify directional shifts with height. Furthermore, diurnal, monthly, and seasonal variations of wind veer characteristics were analysed. Wind shear exponent factor (WSE) and turbulence kinetic energy (TKE) were calculated, and wind veer profiles were constructed based on these parameters. The relative errors of wind speeds were analysed for rotor equivalent wind speed (REWS) and hub height wind speed (HHWS), with REWS with wind veer correction, REWSveer, as a reference. Additionally, atmospheric stability conditions were classified using WSE and TKE, and the vertical changes in wind veering were analysed according to the stability conditions. The findings reveal lower wind speeds exhibited larger wind veer values and fluctuations. The relative errors for the REWS and the HHWS were 0.04 % and 0.20 % on average, respectively. The study demonstrates that terrain conditions significantly impacted wind veer angles at heights below 100 m, whereas the influence diminished with increasing height above 100 m. The results could be helpful for wind farm developers to make decisions on the siting as well as the hub height of wind turbines on complex terrain
Fulltext View|Download
Keywords: Wind data; Ground lidar; Complex terrain; Wind veer; Atmospheric stability
Funding: Jeju National University

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Enhancing microbial fuel cell performance with carbon powder electrode modifications for low-power sensors modules Kinetic and thermodynamic study of composite with jute fiber as reinforcement Investigation of wind veer characteristics on complex terrain using ground-based lidar Policies and measures for energy efficiency improvement at households: A bibliometric analysis More recent articles
Most cited articles
Application of Hydrothermal Treatment to High Concentrated Sewage Sludge for Anaerobic Digestion Process Promotion of Renewable Energy Sources in the European Union Wind Resource Assessment in Abadan Airport in Iran Multi-Feedstocks Biodiesel Production from Esterification of Calophyllum inophyllum Oil, Castor Oil, Palm Oil and Waste Cooking Oil Assessment of Wind Energy Potential in Golestan Province of Iran 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(7), 1838. https://doi.org/10.3390/en11071838
  2. Aghbalou, N., Charki, A., Elazzouzi, S. R., & Reklaoui, K. (2018). A probabilistic assessment approach for wind turbine-site matching. International Journal of Electrical Power & Energy Systems, 103, 497–510. https://doi.org/10.1016/j.ijepes.2018.06.018
  3. Bardal, L. M., Sætran, L. R., & Wangsness, E. (2015). Performance test of a 3MW wind turbine–effects of shear and turbulence. Energy Procedia, 80, 83–91. https://doi.org/10.1016/j.egypro.2015.11.410
  4. Barthelmie, R. J., Shepherd, T. J., Aird, J. A., & Pryor, S. C. (2020). Power and Wind Shear Implications of Large Wind Turbine Scenarios in the US Central Plains. Energies, 13(16). https://doi.org/10.3390/en13164269
  5. Birkelund, Y., Alessandrini, S., Byrkjedal, Ø., & Monache, L. D. (2018). Wind power predictions in complex terrain using analog ensembles. J. Phys.: Conf. Ser. 1102 012008. https://doi.org/10.1088/1742-6596/1102/1/012008
  6. 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(10), 5582–5591. https://doi.org/10.1029/2019GL082636
  7. Borraccino, A., Schlipf, D., Haizmann, F., & Wagner, R. (2017). Wind field reconstruction from nacelle-mounted lidar short-range measurements. Wind Energy Science, 2(1), 269–283. https://doi.org/10.5194/wes-2-269-2017
  8. Durán, P., Meiβner, C., & Casso, P. (2020). A new meso-microscale coupled modelling framework for wind resource assessment: A validation study. Renewable Energy, 160, 538–554. https://doi.org/10.1016/j.renene.2020.06.074
  9. EMD International. (n.d.). EMD-WRF South Korea MesoScale Data Set. https://www.emd-international.com/data-services/mesoscale-time-series/pre-run-time-series/emd-wrf-south-korea-mesoscale-data-set/
  10. Englberger, A., & Lundquist, J. K. (2020). How does inflow veer affect the veer of a wind-turbine wake? Journal of Physics: Conference Series, 1452(1), 12068. https://doi.org/10.1088/1742-6596/1452/1/012068
  11. Ennis, B. L., White, J. R., & Paquette, J. A. (2018). Wind turbine blade load characterization under yaw offset at the SWiFT facility. Journal of Physics: Conference Series, 1037(5), 52001. https://doi.org/10.1088/1742-6596/1037/5/052001
  12. Gao, L., Li, B., & Hong, J. (2021). Effect of wind veer on wind turbine power generation. Physics of Fluids, 33(1), 15101. https://doi.org/10.1063/5.0033826
  13. Gottschall, J., Papetta, A., Kassem, H., Meyer, P. J., Schrempf, L., Wetzel, C., & Becker, J. (2021). Advancing wind resource assessment in complex terrain with scanning lidar measurements. Energies, 14(11), 3280. https://doi.org/10.3390/en14113280
  14. Gualtieri, G., & Secci, S. (2011). Wind shear coefficients, roughness length and energy yield over coastal locations in Southern Italy. Renewable Energy, 36(3), 1081–1094. https://doi.org/10.1016/j.renene.2010.09.001
  15. International Electrotechnical Commission. (2017). Wind turbines, Part 12-1: Power performance measurements of electricity producing wind turbines,. In International Electrotechnical Commission (2nd ed., Vol. 2017). https://webstore.iec.ch/publication/26603
  16. International Electrotechnical Commission. (2022). Wind energy generation systems Part 12-1: Power performance measurements of electricity producing wind turbines. International Electrotechnical Commission, 3. https://www.en-standard.eu/csn-en-iec-61400-12-wind-energy-generation-systems-part-12-power-performance-measurements-of-electricity-producing-wind-turbines-overview/
  17. Jung, C., & Schindler, D. (2021). The role of the power law exponent in wind energy assessment: A global analysis. International Journal of Energy Research, 45(6), 8484–8496. https://doi.org/10.1002/er.6382
  18. Kang, D., Hyeon, J., Yang, K., Huh, J., & Ko, K. (2017). Analysis and Verification of Wind Data from Ground-based LiDAR. Int. J. Renew. Energy Res, 7, 937–945. https://doi.org/10.20508/ijrer.v7i2.6211.g7074
  19. Kikuchi, Y., Fukushima, M., & Ishihara, T. (2020). Assessment of a coastal offshorewind climate by means of mesoscale model simulations considering high-resolution land use and sea surface temperature data sets. Atmosphere, 11(4), 1–16. https://doi.org/10.3390/ATMOS11040379
  20. Kim, D., Kim, T., Oh, G., Huh, J., & Ko, K. (2016). A comparison of ground-based LiDAR and met mast wind measurements for wind resource assessment over various terrain conditions. Journal of Wind Engineering and Industrial Aerodynamics, 158, 109–121. https://doi.org/10.1016/j.jweia.2016.09.011
  21. Leosphere. (2014). Windcube V2 liDAR Remote Sensor User Manual
  22. Lundquist, J. K. (2022). Wind Shear and Wind Veer Effects on Wind Turbines. In Handbook of Wind Energy Aerodynamics (pp. 1–22). Springer. https://doi.org/10.1007/978-3-030-05455-7_44-1
  23. Mason, P. J. (1992). Large-eddy simulation of dispersion in convective boundary layers with wind shear. Atmospheric Environment Part A, General Topics, 26(9), 1561–1571. https://doi.org/10.1016/0960-1686(92)90056-Q
  24. Mortensen, N. G., Tindal, A., & Landberg, L. (2008). Field validation of the RIX performance indicator for flow in complex terrain. 2008 European Wind Energy Conference and Exhibition. https://backend.orbit.dtu.dk/ws/portalfiles/portal/107110613/Field_validation.pdf
  25. 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(3), 1169–1190. https://doi.org/10.5194/wes-5-1169-2020
  26. Nassif, F. B., Pimenta, F. M., Assireu, A. T., D’Aquino, C. de A., & Passos, J. C. (2020). Wind measurements using a LIDAR on a buoy. RBRH, 25. https://doi.org/10.1590/2318-0331.252020200053
  27. Oh, H., & Kim, B. (2015). Comparison and verification of the deviation between guaranteed and measured wind turbine power performance in complex terrain. Energy, 85, 23–29. https://doi.org/10.1016/j.energy.2015.02.115
  28. Radünz, W. C., Sakagami, Y., Haas, R., Petry, A. P., Passos, J. C., Miqueletti, M., & Dias, E. (2020). The variability of wind resources in complex terrain and its relationship with atmospheric stability. Energy Conversion and Management, 222, 113249. https://doi.org/10.1016/j.enconman.2020.113249
  29. Rasaq, A. K., Baba, R. A., Ayomide, G. A., Oladimeji, A. G., & Idris, O. A. (2015). Assessment of wind resource for possibility of small wind turbine installation in Ilorin, Nigeria. KKU Engineering Journal, 42(4), 298–305. https://doi.org/10.14456/kkuenj.2015.35
  30. Rehman, S., & Al-Abbadi, N. M. (2005). Wind shear coefficients and their effect on energy production. Energy Conversion and Management, 46(15–16), 2578–2591. https://doi.org/10.1016/j.enconman.2004.12.005
  31. Robertson, A. N., Shaler, K., Sethuraman, L., & Jonkman, J. (2019). Sensitivity analysis of the effect of wind characteristics and turbine properties on wind turbine loads. Wind Energy Science, 4(3), 479–513. https://doi.org/10.5194/wes-4-479-2019
  32. 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(1), 125–139. https://doi.org/10.5194/wes-5-125-2020
  33. Sanchez Gomez, M., & Lundquist, J. K. (2020b). The Effects of Wind Veer During the Morning and Evening Transitions. Journal of Physics: Conference Series, 1452(1), 12075. https://doi.org/10.1088/1742-6596/1452/1/012075
  34. Sharma, P. K., Gautam, A., Baredar, P., Warudkar, V., Bhagoria, J. L., & Ahmed, S. (2021). Analysis of terrain of site Mamatkheda Ratlam through wind modeling tool ArcGIS and WAsP. Materials Today: Proceedings, 46, 5661–5665. https://doi.org/10.1016/j.matpr.2020.09.638
  35. Shaw, W. J., Berg, L. K., Debnath, M., Deskos, G., Draxl, C., Ghate, V. P., Hasager, C. B., Kotamarthi, R., Mirocha, J. D., & Muradyan, P. (2022). Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer. Wind Energy Science, 7(6), 2307–2334. https://doi.org/10.5194/wes-7-2307-2022
  36. Shin, D., Ko, K., Kang, M. M., Ryu, D., Kang, M. M., & Kim, H. (2019). Comparison of wind turbine power curves using cup anemometer and pulsed doppler light detection and ranging systems. Journal of Mechanical Science and Technology, 33(4), 1663–1671. https://doi.org/10.1007/s12206-019-0318-x
  37. Shu, Z., Li, Q., He, Y., & Chan, P. W. (2020). Investigation of marine wind veer characteristics using wind lidar measurements. Atmosphere, 11(11), 1178. https://doi.org/10.3390/atmos11111178
  38. Shu, Z., Li, Q. S., Chan, P. W., & He, Y. C. (2020). Seasonal and diurnal variation of marine wind characteristics based on lidar measurements. Meteorological Applications, 27(3), e1918. https://doi.org/10.1002/met.1918
  39. Sletsjøe, H. (2020). Complex terrain: from ruggedness index (RIX), towards physical parameterization. Delft University of Technology
  40. St Martin, C. M., Lundquist, J. K., Clifton, A., Poulos, G. S., & Schreck, S. J. (2016). Wind turbine power production and annual energy production depend on atmospheric stability and turbulence. Wind Energy Science, 1(2), 221–236. https://doi.org/10.5194/wes-1-221-2016
  41. Tumenbayar, U., & Ko, K. (2023). An Effect of Wind Veer on Wind Turbine Performance. International Journal of Renewable Energy Development, 12(1). https://doi.org/10.14710/ijred.2023.47905
  42. Wagner, R., Antoniou, I., Pedersen, S. M., Courtney, M. S., & Jørgensen, H. E. (2009). The influence of the wind speed profile on wind turbine performance measurements. Wind Energy, 12(4), 348–362. https://doi.org/10.1002/we.297
  43. Wagner, R., Courtney, M., Gottschall, J., & Lindelöw‐Marsden, P. (2011). Accounting for the speed shear in wind turbine power performance measurement. Wind Energy, 14(8), 993–1004. https://doi.org/10.1002/we.509
  44. Wharton, S., Newman, J. F., Qualley, G., & Miller, W. O. (2015). Measuring turbine inflow with vertically-profiling lidar in complex terrain. Journal of Wind Engineering and Industrial Aerodynamics, 142, 217–231. https://doi.org/10.1016/j.jweia.2015.03.023
  45. Yan, B. W., Li, Q. S., Chan, P. W., He, Y. C., & Shu, Z. R. (2022). Characterising wind shear exponents in the offshore area using Lidar measurements. Applied Ocean Research, 127, 103293. https://doi.org/10.1016/j.apor.2022.103293

Last update:

No citation recorded.

Last update: 2024-11-21 03:40:28

No citation recorded.