An advanced three-dimensional analytical model for wind turbine near and far wake predictions

Linlin Tian, Pengcheng Xiao, Yilei Song, Ning Zhao, Chunling Zhu and Xiyun Lu

Renewable Energy, 2024, vol. 223, issue C

Abstract: Most existing analytical wake models have been designed to represent the velocity distribution of the far wake region behind a wind turbine, but fail to reproduce the trend of the near wake. In order to meet the demand for both near and far wake velocity predictions, a concise three-dimensional dual-cosine shape (3D-COU) model is proposed. Moreover, by taking into account the wind shear and ground effects in the vertical direction, this model is capable of describing the anisotropic property of the 3-D wake field and is much closer to the reality. The proposed model is then calibrated and validated through a series of test cases, including different scales of turbines that are operating under a wide range of inflow conditions, and meanwhile the reference data resource is diverse. Overall, the results demonstrate that the 3D-COU model can accurately predict the variations of the wake velocity in both the vertical and horizontal directions throughout the whole wake region. Given its good accuracy, simplicity and applicability, this model provides the potential to effectively address practical problems in wind energy projects.

Keywords: Wind turbine; Wake model; Wake expansion; Wake deficit; Full wake (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:223:y:2024:i:c:s0960148124001009

DOI: 10.1016/j.renene.2024.120035

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