A new three-dimensional analytical model for wind turbine wake turbulence intensity predictions

Linlin Tian, Yilei Song, Pengcheng Xiao, Ning Zhao, Wenzhong Shen and Chunling Zhu

Renewable Energy, 2022, vol. 189, issue C, 762-776

Abstract: In wind farm design, accurately predicting the wake turbulence level is crucial for turbine power and load evaluation. However, a knowledge gap still exists on the characteristics of wake turbulence, meanwhile there has been little work on the development of related engineering models. In view of this, firstly, one-dimensional analytical models that can estimate the wake width and maximum wake turbulence level at any streamwise positions are proposed and validated. Based on these, a highly simple three-dimensional cosine shape (3D-COTI) model is proposed for estimating the wake turbulence intensity in an effective way. Moreover, by taking into account the wind shear and ground effects, this proposed model is capable of describing the anisotropic property of the 3-D wake field. Afterwards, model evaluations are performed through several test cases consisting various types of turbines operating under a wide range of inflow conditions. Overall, the proposed model shows a good agreement with the measurements about the spatial distribution of turbulence enhancement within the wake flow. Because of its good accuracy, simplicity and universality, the present model has potential for large-scale wind farm applications.

Keywords: Wind turbine wakes; Turbulence intensity; Far wake; Wake development; Wind farm (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:189:y:2022:i:c:p:762-776

DOI: 10.1016/j.renene.2022.02.115

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