Resilient Smart Power Grid Synchronization Estimation Method for System Resilience with Partial Missing Measurements

IF 6.9 2区 工程技术 Q2 ENERGY & FUELS CSEE Journal of Power and Energy Systems Pub Date : 2024-02-14 DOI:10.17775/CSEEJPES.2023.06900
Yi Wang;Yanxin Liu;Mingdong Wang;Venkata Dinavahi;Jun Liang;Yonghui Sun
{"title":"Resilient Smart Power Grid Synchronization Estimation Method for System Resilience with Partial Missing Measurements","authors":"Yi Wang;Yanxin Liu;Mingdong Wang;Venkata Dinavahi;Jun Liang;Yonghui Sun","doi":"10.17775/CSEEJPES.2023.06900","DOIUrl":null,"url":null,"abstract":"With the increasing demand for power system stability and resilience, effective real-time tracking plays a crucial role in smart grid synchronization. However, most studies have focused on measurement noise, while they seldom think about the problem of measurement data loss in smart power grid synchronization. To solve this problem, a resilient fault-tolerant extended Kalman filter (RFTEKF) is proposed to track voltage amplitude, voltage phase angle and frequency dynamically. First, a three-phase unbalanced network's positive sequence fast estimation model is established. Then, the loss phenomenon of measurements occurs randomly, and the randomness of data loss's randomness is defined by discrete interval distribution [0], [1]. Subsequently, a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the time-stamp technique to acquire partial data loss information. Finally, extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter (EKF).","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":6.9000,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10436622","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CSEE Journal of Power and Energy Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10436622/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

With the increasing demand for power system stability and resilience, effective real-time tracking plays a crucial role in smart grid synchronization. However, most studies have focused on measurement noise, while they seldom think about the problem of measurement data loss in smart power grid synchronization. To solve this problem, a resilient fault-tolerant extended Kalman filter (RFTEKF) is proposed to track voltage amplitude, voltage phase angle and frequency dynamically. First, a three-phase unbalanced network's positive sequence fast estimation model is established. Then, the loss phenomenon of measurements occurs randomly, and the randomness of data loss's randomness is defined by discrete interval distribution [0], [1]. Subsequently, a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the time-stamp technique to acquire partial data loss information. Finally, extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter (EKF).
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
针对部分缺失测量的系统弹性智能电网同步估计方法
随着对电力系统稳定性和恢复能力的要求越来越高,有效的实时跟踪在智能电网同步中发挥着至关重要的作用。然而,大多数研究都侧重于测量噪声,而很少考虑智能电网同步中的测量数据丢失问题。为解决这一问题,本文提出了一种弹性容错扩展卡尔曼滤波器(RFTEKF)来动态跟踪电压幅值、电压相位角和频率。首先,建立了三相不平衡电网的正序快速估计模型。然后,随机发生测量丢失现象,数据丢失的随机性由离散区间分布 [0]、[1] 定义。随后,利用时间戳技术获取部分数据丢失信息,设计了基于实时估计框架的弹性容错扩展卡尔曼滤波器。最后,大量仿真结果表明,与传统的扩展卡尔曼滤波器(EKF)相比,所提出的 RFTEKF 能更有效地同步智能电网。
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
CiteScore
11.80
自引率
12.70%
发文量
389
审稿时长
26 weeks
期刊介绍: The CSEE Journal of Power and Energy Systems (JPES) is an international bimonthly journal published by the Chinese Society for Electrical Engineering (CSEE) in collaboration with CEPRI (China Electric Power Research Institute) and IEEE (The Institute of Electrical and Electronics Engineers) Inc. Indexed by SCI, Scopus, INSPEC, CSAD (Chinese Science Abstracts Database), DOAJ, and ProQuest, it serves as a platform for reporting cutting-edge theories, methods, technologies, and applications shaping the development of power systems in energy transition. The journal offers authors an international platform to enhance the reach and impact of their contributions.
期刊最新文献
Transient Voltage Support Strategy of Grid-Forming Medium Voltage Photovoltaic Converter in the LCC-HVDC System Front Cover Contents PFL-DSSE: A Personalized Federated Learning Approach for Distribution System State Estimation Front Cover
×
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