Estimation of wind turbine wakes with generative-adversarial networks

被引：0

作者：

Bove, M. ^{[1
]}

Lopez, B. ^{[1
]}

Toutouh, J. ^{[2
]}

Nesmachnow, S. ^{[3
]}

Draper, M. ^{[1
]}

机构：

[1] Univ Republica, Inst Mecan Fluidos & Ingn Ambiental, Julio Herrera & Reissig 565, Montevideo 11300, Uruguay

[2] Univ Malaga, Inst Tecnol & Ingn Software, Malaga, Spain

[3] Univ Republica, Inst Computac, Julio Herrera & Reissig 565, Montevideo 11300, Uruguay

来源：

WAKE CONFERENCE 2023 | 2023年 / 2505卷

基金：

欧盟地平线“2020”;

关键词：

LARGE-EDDY SIMULATION;

D O I：

10.1088/1742-6596/2505/1/012053

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The interaction of the atmospheric boundary layer with wind turbines and among wind turbines in a wind farm is a complex phenomenon. Its analysis is fundamental for wind energy development. In this work, a generative adversarial network has been developed to predict the mean streamwise velocity component at hub height in the wake of a wind turbine based on the mean streamwise velocity two rotor diameters upstream. The dataset used to train and test the model is obtained from Large Eddy Simulations (LES) of a wind farm comprising 15 wind turbines under different inlet conditions. The method is able to predict accurately the mean streamwise velocity in the wake of one wind turbine. When the model is used to predict the mean streamwise velocity at hub height of the whole wind farm based on the inlet condition, the difference between the model predictions and LES results are larger as looking downstream.

引用

页数：11