唤醒
流入
人工神经网络
计算流体力学
风速
航程(航空)
风力发电
海洋工程
计算机科学
参数统计
功率(物理)
模拟
工程类
机器学习
气象学
航空航天工程
数学
电气工程
统计
物理
量子力学
作者
Kun Yang,Xiaowei Deng,Zilong Ti,Shanghui Yang,Shyh-Jier Huang,Yuhang Wang
标识
DOI:10.1016/j.renene.2023.119240
摘要
This paper presents a data-driven wind farm layout optimization framework that uses a machine learning wake model that considers physical control stages. The machine learning wake model is trained using well-validated Computational Fluid Dynamics (CFD) simulation data, and consists of thousands of sub-models, each of which is an artificial neural network (ANN) wake model. The ANN wake models are trained separately for low-speed and high-speed inflows to ensure high accuracy of the predictions, with less than 2% error compared to CFD simulations. The accuracy and efficiency of the framework are validated, and the results show better agreement with CFD simulation than an analytical wake model developed in recent years. A parametric study on the number of wind turbines in the Horns Rev wind farm demonstrates that more wind turbines can be added with a minor decrease in average power, with more even and staggered layouts. Under full-wake uniform inflow, the selected analytical wake model exhibits a power prediction error of 5%–8%, while the differences between optimal layouts searched by different wake models range from 2% to 8%. When introducing a wider range of inflow direction sectors, the discrepancy between optimal layout performances decreases.
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