一种用于风电场偏航控制应用的数据驱动机器学习方法

IF 3.2 3区 工程技术 Q2 MECHANICS Theoretical and Applied Mechanics Letters Pub Date : 2023-09-01 DOI:10.1016/j.taml.2023.100471
Christian Santoni , Zexia Zhang , Fotis Sotiropoulos , Ali Khosronejad
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引用次数: 0

摘要

本研究提出了一种经济有效的基于机器学习的模型,用于预测风力涡轮机尾迹中的速度和湍流动能场,用于偏航控制应用。该模型由一个自编码器卷积神经网络(ACNN)组成,该网络利用大涡模拟(LES)的瞬时数据训练提取涡轮尾迹特征。将提出的框架应用于桑迪亚国家实验室规模风力农场技术设施,该设施由三个225千瓦的涡轮机组成。对该站点进行了不同风速和偏航角的LES,生成数据集,用于训练和验证所提出的ACNN。结果表明,对于不属于训练过程的涡轮偏航角和风速情况,ACNN能准确预测涡轮尾迹特性。具体而言,ACNN可以准确再现上游涡轮的尾流重定向和下游涡轮的二次尾流转向。与暴力LES相比,本文开发的ACNN将获得风电场稳态一阶和二阶统计量所需的总体计算成本降低了约85%。
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A data-driven machine learning approach for yaw control applications of wind farms

This study proposes a cost-effective machine-learning based model for predicting velocity and turbulence kinetic energy fields in the wake of wind turbines for yaw control applications. The model consists of an auto-encoder convolutional neural network (ACNN) trained to extract the features of turbine wakes using instantaneous data from large-eddy simulation (LES). The proposed framework is demonstrated by applying it to the Sandia National Laboratory Scaled Wind Farm Technology facility consisting of three 225 kW turbines. LES of this site is performed for different wind speeds and yaw angles to generate datasets for training and validating the proposed ACNN. It is shown that the ACNN accurately predicts turbine wake characteristics for cases with turbine yaw angle and wind speed that were not part of the training process. Specifically, the ACNN is shown to reproduce the wake redirection of the upstream turbine and the secondary wake steering of the downstream turbine accurately. Compared to the brute-force LES, the ACNN developed herein is shown to reduce the overall computational cost required to obtain the steady state first and second-order statistics of the wind farm by about 85%.

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来源期刊
CiteScore
6.20
自引率
2.90%
发文量
545
审稿时长
12 weeks
期刊介绍: An international journal devoted to rapid communications on novel and original research in the field of mechanics. TAML aims at publishing novel, cutting edge researches in theoretical, computational, and experimental mechanics. The journal provides fast publication of letter-sized articles and invited reviews within 3 months. We emphasize highlighting advances in science, engineering, and technology with originality and rapidity. Contributions include, but are not limited to, a variety of topics such as: • Aerospace and Aeronautical Engineering • Coastal and Ocean Engineering • Environment and Energy Engineering • Material and Structure Engineering • Biomedical Engineering • Mechanical and Transportation Engineering • Civil and Hydraulic Engineering Theoretical and Applied Mechanics Letters (TAML) was launched in 2011 and sponsored by Institute of Mechanics, Chinese Academy of Sciences (IMCAS) and The Chinese Society of Theoretical and Applied Mechanics (CSTAM). It is the official publication the Beijing International Center for Theoretical and Applied Mechanics (BICTAM).
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