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A new method for improved hub height mean wind speed estimates using short-term hub height data

Author

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  • Lackner, Matthew A.
  • Rogers, Anthony L.
  • Manwell, James F.
  • McGowan, Jon G.

Abstract

The estimation of the wind resource at the hub height of a wind turbine is one of the primary goals of site assessment. Because the measurement heights of meteorological towers (met towers) are typically significantly lower than turbine hub heights, a shear model is generally needed to extrapolate the measured wind resource at the lower measurement height to the hub height of the turbine. This paper presents methods for improving the estimate of the hub height wind resource from met tower data through the use of ground-based remote sensing devices. The methods leverage the two major advantages of these devices: their portability and their ability to measure at the wind turbine hub height. Specifically, the methods rely on augmenting the one year of met tower measurements with short-term measurements from a ground-based remote sensing device. The results indicate that the methods presented are capable of producing substantial improvements in the accuracy and uncertainty of shear extrapolation predictions. The results suggest that the typical site assessment process can be reevaluated, and alternative strategies that utilize ground-based remote sensing devices can be incorporated to significantly improve the process.

Suggested Citation

  • Lackner, Matthew A. & Rogers, Anthony L. & Manwell, James F. & McGowan, Jon G., 2010. "A new method for improved hub height mean wind speed estimates using short-term hub height data," Renewable Energy, Elsevier, vol. 35(10), pages 2340-2347.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:10:p:2340-2347
    DOI: 10.1016/j.renene.2010.03.031
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    Cited by:

    1. Gualtieri, Giovanni & Secci, Sauro, 2012. "Methods to extrapolate wind resource to the turbine hub height based on power law: A 1-h wind speed vs. Weibull distribution extrapolation comparison," Renewable Energy, Elsevier, vol. 43(C), pages 183-200.
    2. Đurišić, Željko & Mikulović, Jovan & Babić, Iva, 2012. "Impact of wind speed variations on wind farm economy in the open market conditions," Renewable Energy, Elsevier, vol. 46(C), pages 289-296.
    3. Vu Dinh, Quang & Doan, Quang-Van & Ngo-Duc, Thanh & Nguyen Dinh, Van & Dinh Duc, Nguyen, 2022. "Offshore wind resource in the context of global climate change over a tropical area," Applied Energy, Elsevier, vol. 308(C).
    4. Katinas, Vladislovas & Sankauskas, Donatas & Markevičius, Antanas & Perednis, Eugenijus, 2014. "Investigation of the wind energy characteristics and power generation in Lithuania," Renewable Energy, Elsevier, vol. 66(C), pages 299-304.
    5. Valsaraj, P. & Thumba, Drisya Alex & Asokan, K. & Kumar, K. Satheesh, 2020. "Symbolic regression-based improved method for wind speed extrapolation from lower to higher altitudes for wind energy applications," Applied Energy, Elsevier, vol. 260(C).
    6. Jung, Sungmoon & Arda Vanli, O. & Kwon, Soon-Duck, 2013. "Wind energy potential assessment considering the uncertainties due to limited data," Applied Energy, Elsevier, vol. 102(C), pages 1492-1503.
    7. Arwade, Sanjay R. & Gioffrè, Massimiliano, 2014. "Validity of stationary probabilistic models for wind speed records of varying duration," Renewable Energy, Elsevier, vol. 69(C), pages 74-81.
    8. Đurišić, Željko & Mikulović, Jovan, 2012. "A model for vertical wind speed data extrapolation for improving wind resource assessment using WAsP," Renewable Energy, Elsevier, vol. 41(C), pages 407-411.
    9. Gualtieri, Giovanni, 2018. "Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height: method's test at a mountain site," Renewable Energy, Elsevier, vol. 120(C), pages 457-467.
    10. José V. P. Miguel & Eliane A. Fadigas & Ildo L. Sauer, 2019. "The Influence of the Wind Measurement Campaign Duration on a Measure-Correlate-Predict (MCP)-Based Wind Resource Assessment," Energies, MDPI, vol. 12(19), pages 1-15, September.
    11. Gualtieri, Giovanni & Secci, Sauro, 2014. "Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy," Renewable Energy, Elsevier, vol. 62(C), pages 164-176.
    12. Díaz, Santiago & Carta, José A. & Matías, José M., 2018. "Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques," Applied Energy, Elsevier, vol. 209(C), pages 455-477.
    13. Nor, Khalid Mohamed & Shaaban, Mohamed & Abdul Rahman, Hasimah, 2014. "Feasibility assessment of wind energy resources in Malaysia based on NWP models," Renewable Energy, Elsevier, vol. 62(C), pages 147-154.
    14. Xu, Wenxuan & Liu, Yongxue & Wu, Wei & Dong, Yanzhu & Lu, Wanyun & Liu, Yongchao & Zhao, Bingxue & Li, Huiting & Yang, Renfei, 2020. "Proliferation of offshore wind farms in the North Sea and surrounding waters revealed by satellite image time series," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    15. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
    16. Assowe Dabar, Omar & Awaleh, Mohamed Osman & Kirk-Davidoff, Daniel & Olauson, Jon & Söder, Lennart & Awaleh, Said Ismael, 2019. "Wind resource assessment and economic analysis for electricity generation in three locations of the Republic of Djibouti," Energy, Elsevier, vol. 185(C), pages 884-894.
    17. Brouwer, Anne Sjoerd & van den Broek, Machteld & Seebregts, Ad & Faaij, André, 2015. "Operational flexibility and economics of power plants in future low-carbon power systems," Applied Energy, Elsevier, vol. 156(C), pages 107-128.
    18. Belabes, B. & Youcefi, A. & Guerri, O. & Djamai, M. & Kaabeche, A., 2015. "Evaluation of wind energy potential and estimation of cost using wind energy turbines for electricity generation in north of Algeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1245-1255.
    19. Chen, Liang, 2020. "Impacts of climate change on wind resources over North America based on NA-CORDEX," Renewable Energy, Elsevier, vol. 153(C), pages 1428-1438.
    20. Troncoso, A. & Salcedo-Sanz, S. & Casanova-Mateo, C. & Riquelme, J.C. & Prieto, L., 2015. "Local models-based regression trees for very short-term wind speed prediction," Renewable Energy, Elsevier, vol. 81(C), pages 589-598.
    21. Gualtieri, Giovanni, 2019. "A comprehensive review on wind resource extrapolation models applied in wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 215-233.
    22. Kwami Senam A. Sedzro & Adekunlé Akim Salami & Pierre Akuété Agbessi & Mawugno Koffi Kodjo, 2022. "Comparative Study of Wind Energy Potential Estimation Methods for Wind Sites in Togo and Benin (West Sub-Saharan Africa)," Energies, MDPI, vol. 15(22), pages 1-28, November.

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