Research and Verification of Three-dimensional Wake Velocity Model for Wind Turbine Based on High-order Gaussian Function

Hong WEI; Zhenzhou ZHAO; Yige LIU; Shangshang WEI; Huiwen LIU; Yan LIU; Qiao LUO

doi:10.13334/j.0258-8013.pcsee.240116

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Research and Verification of Three-dimensional Wake Velocity Model for Wind Turbine Based on High-order Gaussian Function

Renewables and Energy Storage | 更新时间：2026-02-05

- Research and Verification of Three-dimensional Wake Velocity Model for Wind Turbine Based on High-order Gaussian Function
- Proceedings of the CSEE Vol. 45, Issue 13, Pages: 5099-5105(2025)
- 作者机构：
  
  1. 河海大学电气与动力工程学院, 江苏省南京市 211100
  2. 南京工业职业技术大学江苏风力发电工程技术中心, 江苏省南京市 210023
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(52376179);National Natural Science Foundation of China(52106239);Open Fund of Jiangsu Wind Power Engineering Technology Center of China(ZK22-03-01)
- DOI：10.13334/j.0258-8013.pcsee.240116
  CLC： TK83
- Received：16 January 2024，
  
  Online First：23 March 2024，
  
  Published：05 July 2025
- 稿件说明：
移动端阅览
Hong WEI, Zhenzhou ZHAO, Yige LIU, et al. Research and Verification of Three-dimensional Wake Velocity Model for Wind Turbine Based on High-order Gaussian Function[J]. Proceedings of the CSEE, 2025, 45(13): 5099-5105.
DOI：

Hong WEI, Zhenzhou ZHAO, Yige LIU, et al. Research and Verification of Three-dimensional Wake Velocity Model for Wind Turbine Based on High-order Gaussian Function[J]. Proceedings of the CSEE, 2025, 45(13): 5099-5105. DOI： 10.13334/j.0258-8013.pcsee.240116.

摘要

在自然条件下，风速在近尾流区尾流中心近似为顶帽形分布，在远尾流区的分布近似为高斯形。为了提高对尾流分布的预测精度，该文采用高阶高斯函数修正尾流速度分布，建立三维高阶高斯卷吸尾流模型。通过外场试验和风洞试验进行验证，结果表明，该模型能有效描述尾流的径向和垂向空间分布，在不同的风速下，径向相对误差最大为7.29%，最小为1.4%；垂向相对误差最大为7.64%，最小为3.87%，能够较好地预测整个尾流区域的三维分布。

Abstract

Under natural conditions

the wake velocity distribution approximates top-hat shape in the near wake center

and approximates Gaussian shape in the far wake.To improve the prediction accuracy of the wake distribution

a three-dimensional high-order Gaussian Laminar Elongated wake model is established by modifying the wake velocity distribution with high-order Gaussian function. Field tests and wind tunnel tests show that the proposed model can effectively describe the radial and vertical spatial distribution of wake flow. Under different wind speeds

the maximum radial relative error is 7.29% and the minimum is 1.4%; the maximum vertical relative error is 7.64% and the minimum is 3.87%

which means the proposed model can well predict velocity distribution in the whole wake region.

关键词

Keywords

references

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