{"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":"10 1","pages":"427-431"},"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.
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.
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