Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method

IF 6.9 2区 工程技术 Q2 ENERGY & FUELS CSEE Journal of Power and Energy Systems Pub Date : 2023-09-08 DOI:10.17775/CSEEJPES.2023.00230
Chao Ren;Han Yu;Yan Xu;Zhao Yang Dong
{"title":"Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method","authors":"Chao Ren;Han Yu;Yan Xu;Zhao Yang Dong","doi":"10.17775/CSEEJPES.2023.00230","DOIUrl":null,"url":null,"abstract":"This letter proposes a reliable transfer learning (RTL) method for pre-fault dynamic security assessment (DSA) in power systems to improve DSA performance in the presence of potentially related unknown faults. It takes individual discrep-ancies into consideration and can handle unknown faults with incomplete data. Extensive experiment results demonstrate high DSA accuracy and computational efficiency of the proposed RTL method. Theoretical analysis shows RTL can guarantee system performance.","PeriodicalId":10729,"journal":{"name":"CSEE Journal of Power and Energy Systems","volume":null,"pages":null},"PeriodicalIF":6.9000,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10246181","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"CSEE Journal of Power and Energy Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10246181/","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

This letter proposes a reliable transfer learning (RTL) method for pre-fault dynamic security assessment (DSA) in power systems to improve DSA performance in the presence of potentially related unknown faults. It takes individual discrep-ancies into consideration and can handle unknown faults with incomplete data. Extensive experiment results demonstrate high DSA accuracy and computational efficiency of the proposed RTL method. Theoretical analysis shows RTL can guarantee system performance.
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
理解未知故障下电力系统动态安全评估的差异:基于迁移学习的可靠方法
本文提出了一种用于电力系统故障前动态安全评估(DSA)的可靠迁移学习(RTL)方法,以提高 DSA 在潜在相关未知故障情况下的性能。该方法将个体差异考虑在内,可处理数据不完整的未知故障。大量实验结果表明,所提出的 RTL 方法具有较高的 DSA 精度和计算效率。理论分析表明 RTL 可以保证系统性能。
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
Front Cover Contents Proactive Resilience Enhancement of Power Systems with Link Transmission Model-Based Dynamic Traffic Assignment Among Electric Vehicles Assessment and Enhancement of FRC of Power Systems Considering Thermal Power Dynamic Conditions Data-Driven Fault Detection of Multiple Open-Circuit Faults for MMC Systems Based on Long Short-Term Memory Networks
×
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