Optimized wake-superposition approach for multiturbine wind farms

Deshun Li; Jixiang Chang; Gaosheng Ma; Chunyu Huo; Rennian Li

doi:10.1038/s41598-023-33165-4

Optimized wake-superposition approach for multiturbine wind farms

Sci Rep. 2023 Apr 24;13(1):6672. doi: 10.1038/s41598-023-33165-4.

Authors

Deshun Li^{1

2

3}, Jixiang Chang¹, Gaosheng Ma^{4

5

6}, Chunyu Huo¹, Rennian Li^{1

2}

Affiliations

¹ College of Energy and Power Engineering, Lanzhou University of Technology, 287 LanGongPing Road, Qilihe District, Lanzhou, 730050, China.
² Gansu Provincial Technology Centre for Wind Turbines, Lanzhou, 730050, China.
³ Gansu Provincial Key Laboratory of Fluid Machinery and Systems, Lanzhou, 730050, China.
⁴ College of Energy and Power Engineering, Lanzhou University of Technology, 287 LanGongPing Road, Qilihe District, Lanzhou, 730050, China. magsh@lzu.edu.cn.
⁵ Gansu Provincial Technology Centre for Wind Turbines, Lanzhou, 730050, China. magsh@lzu.edu.cn.
⁶ Gansu Provincial Key Laboratory of Fluid Machinery and Systems, Lanzhou, 730050, China. magsh@lzu.edu.cn.

Abstract

Optimizing the wind farm layout requires accurately quantifying the wind-turbine wake distribution to minimize interference between wakes. Thus, the accuracy of wind turbine wake superposition models is critical. The sum of squares (SS) model is currently touted as the most accurate, but its application in engineering is hampered by its overestimation of the velocity deficit of the mixed wake. Therefore, previous work relied on approximate power calculations for performing optimization. The physical meaning of the SS model is unclear, which makes optimization difficult. In this study, a univariate linear correction idea is proposed based on the linear increase phenomenon of the SS method error. The unknown coefficients are obtained by fitting experimental data. The results demonstrate that the proposed method can accurately quantify the full-wake two-dimensional distribution of the mixed wake.

Abstract

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