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Advancement of an analytical double-Gaussian full wind turbine wake model

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  • Keane, Aidan

Abstract

A recently proposed analytical wake model for a horizontal axis utility scale wind turbine is revisited, and revised and improved. The model is based upon conservation of momentum in the context of actuator disc theory, and the assumption of a distribution of the double-Gaussian type for the velocity deficit in the wake. The model is developed and improved and reveals characteristics of the wind turbine wake velocity deficit for the full wake, including the near-wake to within close proximity of the wind turbine rotor. Full 2-dimensional model fitting to lidar wake measurement data obtained from a 5 MW utility scale wind turbine is carried out for the full range of inflow wind velocities of primary interest. Such a full wind turbine wake model has the potential to facilitate analytic calculations within the wind turbine wake region, and the potential to improve understanding of wind turbine aerodynamics.

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  • Keane, Aidan, 2021. "Advancement of an analytical double-Gaussian full wind turbine wake model," Renewable Energy, Elsevier, vol. 171(C), pages 687-708.
    • Handle: RePEc:eee:renene:v:171:y:2021:i:c:p:687-708
    DOI: 10.1016/j.renene.2021.02.078
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    1. Guo-Wei Qian & Takeshi Ishihara, 2018. "A New Analytical Wake Model for Yawed Wind Turbines," Energies, MDPI, vol. 11(3), pages 1-24, March.
    2. Bastankhah, Majid & Porté-Agel, Fernando, 2014. "A new analytical model for wind-turbine wakes," Renewable Energy, Elsevier, vol. 70(C), pages 116-123.
    3. Amin Niayifar & Fernando Porté-Agel, 2016. "Analytical Modeling of Wind Farms: A New Approach for Power Prediction," Energies, MDPI, vol. 9(9), pages 1-13, September.
    4. Göçmen, Tuhfe & Laan, Paul van der & Réthoré, Pierre-Elouan & Diaz, Alfredo Peña & Larsen, Gunner Chr. & Ott, Søren, 2016. "Wind turbine wake models developed at the technical university of Denmark: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 752-769.
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