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An experimental test of the empirical formulae commonly used to represent wind speed profiles near the ground

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  • Pneumatikos, John D.

Abstract

The search for viable alternative energy sources has led to many surveys of wind power resources using wind data at a single reference height (usually 10 m). However, wind speed data must be expressed at the height range of 16–30 m which is the hub height of most operating WECS. The accuracy of different methods (power law, logarithmic, log-linear) for extrapolating wind speed at WECS hub height is investigated. Wind speed and temperature profiles from a 32 m tower have been used to provide the data set needed for this study. Terrain characteristics of the area around the tower have a mean roughness parameter of 34 cm. Power law exponents, for a wide range of stability cases, have a mean value of 0.33 and diverge considerably from the traditional 17 exponent. The log-linear relation reproduces up to 87% the wind speed at hub height. On the contrary, when the reference level is at least an order of magnitude higher than the roughness elements of the surrounding area, the power law reproduces the hub height wind speed in 68–82% of the cases.

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  • Pneumatikos, John D., 1991. "An experimental test of the empirical formulae commonly used to represent wind speed profiles near the ground," Renewable Energy, Elsevier, vol. 1(5), pages 623-628.
    • Handle: RePEc:eee:renene:v:1:y:1991:i:5:p:623-628
    DOI: 10.1016/0960-1481(91)90006-B
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    1. Pneumatikos, John D., 1991. "Wind energy potential in NW Peloponnese—Greece," Renewable Energy, Elsevier, vol. 1(1), pages 137-139.
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    1. Haralambopoulos, D.A. & Lekkas, Th. & Haloulakos, G., 1997. "An investigation of wind electricity production to cover the energy needs of a solar evaporation salt plant," Renewable Energy, Elsevier, vol. 11(1), pages 1-13.
    2. 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.
    3. Gualtieri, Giovanni & Secci, Sauro, 2011. "Comparing methods to calculate atmospheric stability-dependent wind speed profiles: A case study on coastal location," Renewable Energy, Elsevier, vol. 36(8), pages 2189-2204.
    4. Bezrukovs, Valerijs & Zacepins, Aleksejs & Bezrukovs, Vladislavs & Komasilovs, Vitalijs, 2016. "Comparison of methods for evaluation of wind turbine power production by the results of wind shear measurements on the Baltic shore of Latvia," Renewable Energy, Elsevier, vol. 96(PA), pages 765-774.
    5. Gualtieri, Giovanni, 2016. "Atmospheric stability varying wind shear coefficients to improve wind resource extrapolation: A temporal analysis," Renewable Energy, Elsevier, vol. 87(P1), pages 376-390.
    6. 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.
    7. 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).

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