Efficient electromagnetic transient simulation for DFIG-based wind farms using fine-grained network partitioning

IF 5 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC International Journal of Electrical Power & Energy Systems Pub Date : 2024-10-16 DOI:10.1016/j.ijepes.2024.110297
Jiale Yu, Haoran Zhao, Yibao Jiang, Bing Li, Linghan Meng, Futao Yang
{"title":"Efficient electromagnetic transient simulation for DFIG-based wind farms using fine-grained network partitioning","authors":"Jiale Yu,&nbsp;Haoran Zhao,&nbsp;Yibao Jiang,&nbsp;Bing Li,&nbsp;Linghan Meng,&nbsp;Futao Yang","doi":"10.1016/j.ijepes.2024.110297","DOIUrl":null,"url":null,"abstract":"<div><div>Electromagnetic transient (EMT) simulation plays a critical role in understanding the dynamic behavior and fast transients involved in wind farms (WFs). However, as WFs continue to develop on a large scale, the increasing number of wind turbines and network nodes poses significant challenges for efficient EMT simulation of WFs. To address this issue, we propose a fine-grained network decoupling method for doubly-fed induction generator (DFIG) based WFs. This paper first establishes the decoupling algorithm for core electrical equipment of DFIG-based WFs. By employing device-level fine-grained decoupling, the dimensionality of the admittance matrix for WF is effectively reduced, significantly decreasing the computational load. Additionally, this paper establishes a scalable computational framework by integrating multi-threaded parallel computation into the simulation process, which enhances efficiency further. The proposed method is compared with detailed models in Matlab/Simulink to verify efficiency and accuracy. Simulation results demonstrate that this method significantly improves simulation efficiency, achieving a two-order-of-magnitude speedup with 50 wind turbines, and it maintains high simulation accuracy, with a maximum relative error of 1.68%.</div></div>","PeriodicalId":50326,"journal":{"name":"International Journal of Electrical Power & Energy Systems","volume":"162 ","pages":"Article 110297"},"PeriodicalIF":5.0000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Electrical Power & Energy Systems","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0142061524005192","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Electromagnetic transient (EMT) simulation plays a critical role in understanding the dynamic behavior and fast transients involved in wind farms (WFs). However, as WFs continue to develop on a large scale, the increasing number of wind turbines and network nodes poses significant challenges for efficient EMT simulation of WFs. To address this issue, we propose a fine-grained network decoupling method for doubly-fed induction generator (DFIG) based WFs. This paper first establishes the decoupling algorithm for core electrical equipment of DFIG-based WFs. By employing device-level fine-grained decoupling, the dimensionality of the admittance matrix for WF is effectively reduced, significantly decreasing the computational load. Additionally, this paper establishes a scalable computational framework by integrating multi-threaded parallel computation into the simulation process, which enhances efficiency further. The proposed method is compared with detailed models in Matlab/Simulink to verify efficiency and accuracy. Simulation results demonstrate that this method significantly improves simulation efficiency, achieving a two-order-of-magnitude speedup with 50 wind turbines, and it maintains high simulation accuracy, with a maximum relative error of 1.68%.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用细粒度网络分区对基于 DFIG 的风电场进行高效电磁暂态仿真
电磁瞬态(EMT)仿真在了解风电场(WFs)的动态行为和快速瞬态方面发挥着至关重要的作用。然而,随着风电场的不断大规模发展,风力涡轮机和网络节点数量的不断增加给风电场的高效 EMT 仿真带来了巨大挑战。为解决这一问题,我们提出了一种基于双馈感应发电机(DFIG)的风力发电机细粒度网络解耦方法。本文首先建立了基于 DFIG 的风力发电机核心电气设备的解耦算法。通过采用设备级细粒度解耦,有效降低了 WF 的导纳矩阵维度,从而显著减少了计算负荷。此外,本文通过将多线程并行计算集成到仿真过程中,建立了一个可扩展的计算框架,从而进一步提高了效率。本文提出的方法与 Matlab/Simulink 中的详细模型进行了比较,以验证其效率和准确性。仿真结果表明,该方法显著提高了仿真效率,在使用 50 个风力涡轮机的情况下,仿真速度提高了两个数量级,并且保持了较高的仿真精度,最大相对误差为 1.68%。
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 去求助
来源期刊
International Journal of Electrical Power & Energy Systems
International Journal of Electrical Power & Energy Systems 工程技术-工程:电子与电气
CiteScore
12.10
自引率
17.30%
发文量
1022
审稿时长
51 days
期刊介绍: The journal covers theoretical developments in electrical power and energy systems and their applications. The coverage embraces: generation and network planning; reliability; long and short term operation; expert systems; neural networks; object oriented systems; system control centres; database and information systems; stock and parameter estimation; system security and adequacy; network theory, modelling and computation; small and large system dynamics; dynamic model identification; on-line control including load and switching control; protection; distribution systems; energy economics; impact of non-conventional systems; and man-machine interfaces. As well as original research papers, the journal publishes short contributions, book reviews and conference reports. All papers are peer-reviewed by at least two referees.
期刊最新文献
Microgrid energy management strategy considering source-load forecast error A dimension-enhanced residual multi-scale attention framework for identifying anomalous waveforms of fault recorders Grid structure optimization using slow coherency theory and holomorphic embedding method Two-stage multi-objective optimal dispatch of hybrid power generation system for ramp stress mitigation A fast, simple and local protection scheme for fault detection and classification during power swings based on differential current component
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1