A Bayesian Deep Learning-Based Adaptive Wind Farm Power Prediction Method Within the Entire Life Cycle

IF 8.6 1区 工程技术 Q1 ENERGY & FUELS
Xiaoming Liu;Jun Liu;Yu Zhao;Yongxin Nie;Jiacheng Liu;Tao Ding
{"title":"A Bayesian Deep Learning-Based Adaptive Wind Farm Power Prediction Method Within the Entire Life Cycle","authors":"Xiaoming Liu;Jun Liu;Yu Zhao;Yongxin Nie;Jiacheng Liu;Tao Ding","doi":"10.1109/TSTE.2024.3435936","DOIUrl":null,"url":null,"abstract":"Accurate wind power prediction (WPP) is crucial to the secure and stable operation of large-scale power systems, and data-driven WPP methods have recently been widely studied and applied. However, existing data-driven methods cannot be applied to new wind farms due to the lack of operational data. This paper presents a novel Bayesian deep learning-based adaptive wind farm power prediction (BDL-AWFPP) method, which is the first time to utilize the computational fluid dynamics (CFD) simulation results as the prior of BDL-based method, thus avoiding the problem that data-driven approaches cannot be applied to newly constructed wind farms. Firstly, a CFD-based wind farm numerical simulation database and a wind turbine power curve database are established to construct a multi-source heterogeneous prior dataset. Then, the BDL-AWFPP model is proposed to utilize the multi-source heterogeneous prior dataset, which can be updated adaptively with newly acquired operational data and saved periodically throughout the life cycle. And an auxiliary aging assessment method for wind turbines is also developed according to the periodically-saved models. Finally, a stochastic variational inference (SVI)-based parameter updating algorithm is derived for the proposed BDL-AWFPP model. Case studies on an actual wind farm validate the effectiveness of the proposed method.","PeriodicalId":452,"journal":{"name":"IEEE Transactions on Sustainable Energy","volume":"15 4","pages":"2663-2674"},"PeriodicalIF":8.6000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Sustainable Energy","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10614916/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

Accurate wind power prediction (WPP) is crucial to the secure and stable operation of large-scale power systems, and data-driven WPP methods have recently been widely studied and applied. However, existing data-driven methods cannot be applied to new wind farms due to the lack of operational data. This paper presents a novel Bayesian deep learning-based adaptive wind farm power prediction (BDL-AWFPP) method, which is the first time to utilize the computational fluid dynamics (CFD) simulation results as the prior of BDL-based method, thus avoiding the problem that data-driven approaches cannot be applied to newly constructed wind farms. Firstly, a CFD-based wind farm numerical simulation database and a wind turbine power curve database are established to construct a multi-source heterogeneous prior dataset. Then, the BDL-AWFPP model is proposed to utilize the multi-source heterogeneous prior dataset, which can be updated adaptively with newly acquired operational data and saved periodically throughout the life cycle. And an auxiliary aging assessment method for wind turbines is also developed according to the periodically-saved models. Finally, a stochastic variational inference (SVI)-based parameter updating algorithm is derived for the proposed BDL-AWFPP model. Case studies on an actual wind farm validate the effectiveness of the proposed method.
基于贝叶斯深度学习的全生命周期自适应风电场功率预测方法
准确的风功率预测(WPP)对大型电力系统的安全稳定运行至关重要,数据驱动的风功率预测方法近年来得到了广泛的研究和应用。然而,由于缺乏运行数据,现有的数据驱动方法无法应用于新风场。本文提出了一种新颖的基于贝叶斯深度学习的自适应风电场功率预测(BDL-AWFPP)方法,首次将计算流体动力学(CFD)仿真结果作为基于BDL方法的先验,从而避免了数据驱动方法无法应用于新建风电场的问题。首先,建立基于 CFD 的风电场数值模拟数据库和风机功率曲线数据库,构建多源异构先验数据集。然后,提出 BDL-AWFPP 模型来利用多源异构先验数据集,该数据集可根据新获取的运行数据进行自适应更新,并在整个生命周期内定期保存。根据定期保存的模型,还开发了风力涡轮机的辅助老化评估方法。最后,为所提出的 BDL-AWFPP 模型推导出了一种基于随机变量推理(SVI)的参数更新算法。对实际风电场的案例研究验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Transactions on Sustainable Energy
IEEE Transactions on Sustainable Energy ENERGY & FUELS-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
21.40
自引率
5.70%
发文量
215
审稿时长
5 months
期刊介绍: The IEEE Transactions on Sustainable Energy serves as a pivotal platform for sharing groundbreaking research findings on sustainable energy systems, with a focus on their seamless integration into power transmission and/or distribution grids. The journal showcases original research spanning the design, implementation, grid-integration, and control of sustainable energy technologies and systems. Additionally, the Transactions warmly welcomes manuscripts addressing the design, implementation, and evaluation of power systems influenced by sustainable energy systems and devices.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信