DOI: https://doi.org/10.14710/ijred.9.2.263-286

Assessment of IEC Normal Turbulence Model and Modelling of the Wind Turbulence Intensity for Small Wind Turbine Design in Tropical Area: Case of the Coastal Region of Benin

1Laboratoire de Physique du Rayonnement (LPR), Faculté des Sciences et Techniques (FAST), Université d’Abomey-Calavi, 01 B.P.526, Cotonou, Benin

2Laboratoire de Physique du Rayonnement (LPR), Faculté des Sciences et Techniques (FAST),, Benin

3Université d’Abomey-Calavi, 01 B.P.526, Cotonou,, Benin

Received: 3 Jan 2020; Revised: 7 Apr 2020; Accepted: 12 Apr 2020; Available online: 13 May 2020; Published: 15 Jul 2020.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2020 The Authors. 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 turbulence intensity observed on a site have an influence the wind turbine energy production and the lifetime of the blades. It is therefore primordial to master this parameter for the optimization of the production. So therefore, this study is interested on the modelling of the wind turbulence intensity at 10 m above the ground on the coast of Benin. Four years of wind data measured on the site of Cotonou Port Authority (PAC) from 2011 to 2014 and recorded with a temporal resolution of 10 min were used. From the transport equation of turbulent kinetic energy followed by a numerical simulation based on the Nelder-Mead algorithm developed under the Matlab software, we proposed five new models for estimating the wind turbulence intensity. The results of the different simulations reveal that four of proposed models and based on the roughness, the speed of friction and the length of Obukhov better fit the data, during the periods of January, April, June, July, August, September and December. The estimators of the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) vary from (0.02; 0.01) in December to (0.09; 0.07) in August. As for the model  which is a function of roughness and the wind  shear coefficient (expressed only according to the wind speed), it gives better performance whatever the time of the year and the atmosphere stability conditions. The estimations errors are included between (0.02; 0.01) obtained in December and (0.08; 0.06) observed in March. A comparative study between the existing models in the literature and the best model proposed in this study showed that only this model gives the best adjustment with the data. It can therefore be used on the sites where turbulence is influenced by the roughness and the atmosphere stability. Finally, from this model a new wind turbine design class has been proposed for the site of Cotonou. It takes into account the actual levels of turbulence observed and thus allow to optimize the energy production. 

Fulltext View|Download
Keywords: Atmosphere; Model; Simulation; Turbulence intensity; Wind speed

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Estimating Weibull Parameters for Wind Energy Applications using Seven Numerical Methods: Case studies of three costal sites in West Africa Bioethanol Production from Sugarcane Bagasse Using Neurospora intermedia in an Airlift Bioreactor Evaluation of the Wind Potential and Optimal Design of a Wind Farm in The Arzew Industrial Zone in Western Algeria Distributed Generation: A Critical Review of Technologies, Grid Integration Issues, Growth Drivers and Potential Benefits More recent articles
Most cited articles
Analytical Investigations of Kinetic and Heat Transfer in Slow Pyrolysis of a Biomass Particle Passive Design of Buildings for Extreme Weather Environment Design and Development of Prototype Cylindrical Parabolic Solar Collector for Water Heating Application Woodfuel in Rwanda: Impact on Energy, Poverty, Environment and Policy Instruments analysis The characteristic changes of betung bamboo (Dendrocalamus asper) pretreated by fungal pretreatment More cited articles
  1. Akpo, A. B., Damada, J. C. T., Donnou, H. E. V., Kounouhewa, B. and Awanou C. N. (2015) Estimation de la production énergétique d'un aérogénérateur sur un site isolé dans la région côtière du Bénin. Revue des Energies Renouvelables, 18(3), 457-468
  2. Amar, F. B., Elamouri M, and Dhifaoui R. (2008) Energy assessment of the first wind farm section of Sidi Daoud, Tunisia. Renewable Energy, 33(10), 2311-2321 https://doi.org/10.1016/j.renene.2007.12.019
  3. Awanou, C. N., Degbey, J. M and Ahlonsou, E. (1991) Estimation of the mean wind energy avaible in Benin (ex Dahomey). Renew. Energy, 1 5/6 40, 845-853. https://doi.org/10.1016/0960-1481(91)90037-P
  4. Barthelmie, R. J., Frandsen, S. T and Nielsen, M. N. (2007) Modelling and Measurements of Power Losses and Turbulence Intensity in Wind Turbine Wakes at Middelgrunden Offshore Wind Farm. Wind Energ, 10: 517-528. https://doi.org/10.1002/we.238
  5. Businger, J. A., Wyngaard, J. C., Izumi, Y. and Bradley, E. F. (1971) Flux-profile relationships in the atmospheric surface layer. J Atmos Sci, 28, 181-189. https://doi.org/10.1175/1520-0469(1971)028<0181:FPRITA>2.0.CO;2
  6. Carpman, N. (2011) Turbulence intensity in complex environments and its influence on small wind turbines. M.Sc. Dissertation. Department of Earth Sciences, Uppsala University
  7. Chai, T., and Draxler, R. R., (2014) Root mean square error (RMSE) or mean absolute error (MAE)?-arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3); 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
  8. Cheung, L. C., Premasuthan, S., Davoust, S., von Terzi D. (2016) A Simple Model for the Turbulence Intensity Distribution in Atmospheric Boundary Layers. Journal of Physics: Conference Series 753 032008. https://doi.org/10.1088/1742-6596/753/3/032008
  9. Corless, R. M. and Jeffrey, D. J. (2002) The Wright omega Function. Conference: Artificial Intelligence, Automated Reasoning, and Symbolic Computation,'' Joint International Conferences, AISC 2002 and Calculemus 2002, Marseille, France, July 1-5 Proceedings. J. Calmet et al (Eds.): AISC-Calculemus 2002, LNAI 2385, pp. 76-89 Springer-Verlag. (2002)
  10. Darbieu, C., Lohou, F., Lothon, M., Vilà-Guerau de Arellano, J., Couvreux, F., Durand, P., Pino, D., Patton, E. G., Nilsson, E., Blay-Carreras, E. and Gioli, B. (2014) Turbulence vertical structure of the boundary layer during the afternoon transition. Atmos. Chem. Phys. Discuss, 14, 32491-32533. https://doi.org/10.5194/acpd-14-32491-2014
  11. Dimitrov, N., Natarajan, A. and Kelly M. (2015) Model of wind shear conditional on turbulence and its impact on wind turbine loads. Wind Energ 2015; 18, 1917-1931. https://doi.org/10.1002/we.1797
  12. Dimitrov, N., Natarajan, A. and Mann, J. (2017) Effects of normal and extreme turbulence spectral parameters on wind turbine loads. Renewable Energy, 101, 1180- 1193. https://doi.org/10.1016/j.renene.2016.10.001
  13. Donnou, H. E. V., Akpo, A. B., Djossou, J., Kounouhewa, B. B. (2019c) Wind turbulence intensity characteristics at 10 m above ground along the Cotonou coast, Benin. International journal of sustainable and green energy, 8(4), 65-80
  14. Donnou, H. E. V., Akpo, A. B., Kouchadé, A. C., Kounouhewa, B. B., Hounguè, G. H. Nonfodji, G. F. and J. Djossou. (2019a) Vertical profile of wind diurnal cycle in the surface boundary layer over the coast of Cotonou, Benin, under a convective atmosphere. Advances in Meteorology, 2019, 1-18. https://doi.org/10.1155/2019/7530828
  15. Dyer, A. J. (1974) A review of flux-profile relationships. Boundary-Layer Meteorol, 7, 363-372. https://doi.org/10.1007/BF00240838
  16. Evans, S. P., Anup, K. C., Bradney, D. R., Urmee, T. P. Whale, J. and P. D. Clausen P. D. (2017) The suitability of the IEC 61400-2 wind model for small wind turbines operating in the built environment. Renew. Energy Environ. Sustain, 2(31), 1-7. https://doi.org/10.1051/rees/2017022
  17. Finnigan J. J. (1994). Atmospheric boundary layer flows. Oxford University Press
  18. Gottschall, J. and Peinke, J. (2008) How to improve the estimation of power curves for wind turbines. Environmental Research Letters, 3(1), 15005-15007. https://doi.org/10.1088/1748-9326/3/1/015005
  19. Gryning, S. E., Batchvarova, E., Brümmer, B., Jørgensen, H. and Larsen, S. (2007) On the extension of the wind profile over homogeneous terrain beyond the surface layer. Boundary-Layer Meteorol, 124, 251-268. https://doi.org/10.1007/s10546-007-9166-9
  20. Gualtieri, G. (2015) Turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height. Renewable Energy, 78, 68-81. https://doi.org/10.1016/j.renene.2015.01.011
  21. Hedevang, E. (2014) Wind turbine power curves incorporating turbulence intensity. Wind Energy, 17(2), 173-195. https://doi.org/10.1002/we.1566
  22. Högström, U. (1988) Non-dimensional wind and temperature profiles in the atmospheric surface layer: a re-evaluation. Boundary-Layer Meteorol, 42, 55-78. https://doi.org/10.1007/BF00119875
  23. Houekpoheha, M. A., Kounouhewa, B., Tokpohozin, B. N., and Awanou C. N. (2014) Estimation de la puissance énergétique éolienne à partir de la distribution de weibull sur la côte béninoise de Cotonou dans le golfe de guinée. Revue des Energies Renouvelables, 17, 489-495
  24. Houngninou, B. E., Allé, C. S. U., Guédjé, K. F., and Kougbéagbédè, H. (2017) Changes in near-surface wind speed in the south of Benin from 1961 to 2016. Int. J. Adv. Res, 5(11), 1223-1232
  25. Hounguè, G. H., Kounouhewa, B. B., Tokpohozin, B. N., Houekpoheha, M. A., Madogni, V. I. and Almar, R. (2018) Wave Energy Impact on Benin's Coastline Dynamics, Gulf of Guinea. Current Journal of Applied Science and Technology, 30(4), 1-12. https://doi.org/10.9734/CJAST/2018/44341
  26. IEC 61400.2-2013. Wind Turbines Part 2. Design Requirements for Small Wind Turbines Australia Standard: Australia, 2013
  27. Ishihara, T., Yamaguchi, A., Oikawa, S., Sarwar, M. W. (2012) A Study of the Normal Turbulence Model in IEC61400-1. Wind Energy, 96, 142-147
  28. Kaiser, K., Langreder, W., Hohlen, H. and Højstrup J. (2004) Turbulence Correction for Power Curves. Wind Energy, 159-162. https://doi.org/10.1007/978-3-540-33866-6_28
  29. Kamada, Y., Maeda, T., Murata, J., Toki, T. and Tobuchi A. (2011) Effects of Turbulence Intensity on Dynamic Characteristics of Wind Turbine Airfoil. Journal of Fluid Science and Technology, 6(3), 333-341. https://doi.org/10.1299/jfst.6.333
  30. Kim, S. H., Shin, H. K., Joo, Y. C. and Kim K. H. (2015) A study of the wake effects on the wind characteristics and fatigue loads for the turbines in a wind farm. Renewable Energy, 74, 536-543. https://doi.org/10.1016/j.renene.2014.08.054
  31. Lagarias, J. C., Reeds, J. A., Wright, M. H., and Wright, P. E. (1998) Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions. SIAM Journal of Optimization, 9(1), 112-147. https://doi.org/10.1137/S1052623496303470
  32. Leu, T. S., Yo, J. M., Tsai, Y. T., Miau, J. J. and Wang, T. C. (2014) Assessment of IEC 61400-1 normal turbulence model for wind conditions in Taiwan West Coast areas,'' 5th International Symposium on Physics of Fluids (ISPF5) International Journal of Modern Physics: Conference Series 34 doi: 10.1142/S2010194514603822.https://doi.org/10.1142/S2010194514603822
  33. Lopez-Villalobos, C. A., Hernandez-Cruz, O. R., Jaramillo, O. A., Mendoza J. L. (2018) Wind Turbulence Intensity at La Ventosa, Mexico: A Comparative Study with the IEC61400 Standards. Energies, 11(11), 1-19 https://doi.org/10.3390/en11113007
  34. Madougou, S. (2010) Etude du potentiel éolien du jet nocturne dans la zone sahélienne à partir des observations de radars profilers de vent. Thèse de Doctorat Université de Toulouse, France
  35. Marino, E., Giusti, A. and Manuel, L. (2017) Offshore wind turbine fatigue loads: The influence of alternative wave modeling for different turbulent and mean winds. Renewable Energy, 102, 157-169. https://doi.org/10.1016/j.renene.2016.10.023
  36. Mirhosseini, M., Sharifi, F. and Sedaghat, A. (2011) Assessing the wind energy potential locations in province of Semnan in Iran. Renew Sustain Energy Rev, 15: 449-59. https://doi.org/10.1016/j.rser.2010.09.029
  37. Nelder, J. A. and Mead, R. A. (1965) simplex method for function minimization. Computer Journal, 7, 308-313. https://doi.org/10.1093/comjnl/7.4.308
  38. Newman, J. F. and Klein, P. M. (2014) The impacts of atmospheric stability on the accuracy of wind speed extrapolation methods. Resources, 3(1), 81-105. https://doi.org/10.3390/resources3010081
  39. Panofsky, H. A. (1973) 'Tower micrometeorogy. In: Haugeb DA (ed) Workshop on micrometeorolgy, American Meteorology Society, 151-176
  40. Peña A, Floors R, Sathe A, Gryning, S. E. , R. Wagner, R., Courtney, M. S., Hahmann, X. G. L. A. N. and Hasager, C. B. (2015) Ten Years of Boundary-Layer and Wind-Power Meteorology at Høvsøre. Denmark. Boundary-Layer Meteorol, 158(1), 1-26. https://doi.org/10.1007/s10546-015-0079-8
  41. Peña, A., Gryning, S. E., Charlotte, B., and Hasager, (2008) Measurements and Modelling of the Wind Speed Profile in the Marine Atmospheric Boundary Layer. Boundary-Layer Meteorol, 129: 479-495. https://doi.org/10.1007/s10546-008-9323-9
  42. POPE, S. B. TURBULENT FLOWS. CAMBRIDGE UNIVERSITY PRESS. 771 PP. ISBN 0 521. HTTPS://DOI.ORG/10.1017/S0022112000212913, 2000.
  43. Ren, G, Liu J, Wan, J., Li, F., Y. Guo, Y. and Yu D. (2018) The analysis of turbulence intensity based on wind speed data in onshore wind farms. Renewable Energy, doi: 10.1016/j.renene.2018.02.080.https://doi.org/10.1016/j.renene.2018.02.080
  44. Richard, E. and Dolle, A. (2011) Data Report (MCA): Development of a metocean station at the port of 579 Cotonou: Supply, installation, operation and maintenance of an oceanographic monitoring: (lot5),'' 580 pp8-atm-145c, 65p
  45. Rosen, A. and Sheinman, Y. (1994) The average output power of a wind turbine in a turbulent wind. Journal of Wind Engineering & Industrial Aerodynamics, 51(3), 287- 302. https://doi.org/10.1016/0167-6105(94)90064-7
  46. Siddiqui, M. S., Rasheed, A., Kvamsdal, T., and Tabib M. (2015) Effect of Turbulence Intensity on the Performance of an Offshore Vertical Axis Wind Turbine. Energy Procedia, 80, 312-320. https://doi.org/10.1016/j.egypro.2015.11.435
  47. Smedman-Högström, A. S., and Högström, U. A. (1983) A practical method for determining wind frequency distributions for the lowest 200 m from routine meteorological data. J Appl Meteor, 17, 942-954. https://doi.org/10.1175/1520-0450(1978)017<0942:APMFDW>2.0.CO;2
  48. Sonia, W. and Lundquist, J. K. (2012) Assessing Atmospheric Stability and its Impacts on Rotor-Disk Wind Characteristics at an Onshore Wind Farm. Wind Energy, 15(4), 525-546. https://doi.org/10.1002/we.483
  49. Spera, D. A., and Richards, T. R. (1979) Modified power law equations for vertical wind profiles. In: Conference and workshop on wind energy characteristics and wind energy siting. Portland, OR, USA
  50. Stival, L. J. L., Guetter, A. K. and de Andrade F. O. (2017) The impact of wind shear and turbulence intensity on wind turbine power performance. Espaço Energia, 27, 11-20
  51. Stull, R. B. (1988) An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers. https://doi.org/10.1007/978-94-009-3027-8
  52. Turk, M. and Emeis, S. (2010) The dependence of offshore turbulence intensity on wind speed. J. Wind Eng. Ind. Aerodyn. 98, 466-471. https://doi.org/10.1016/j.jweia.2010.02.005
  53. Wang, H., Barthelmie, R. J., Pryor, S. C., and Kim, H. G. (2013) A new turbulence model for offshore wind turbine standards. Wind Energy, 17(10), 1587-1604. https://doi.org/10.1002/we.1654

Last update:

  1. Analysis of urban turbulence intensity observed by Beijing 325-m tower and comparison with the IEC turbulence model for small wind turbines

    Zhe Zhang, Lei Liu, Yu Shi, Kang Zhang, Kewei Liu, Shu Wang, Fei Hu. Journal of Wind Engineering and Industrial Aerodynamics, 241 , 2023. doi: 10.1016/j.jweia.2023.105511

Last update: 2024-05-08 12:55:48

No citation recorded.