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

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外文摘要：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.

外文关键词：

Adversarial trainingdynamic security assessmentmaximum classifier discrepancymissing datatransfer learning

作者：

Chao Ren、Han Yu、Yan Xu、Zhao Yang Dong

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作者单位：

School of Computer Science and Engineering,Nanyang Technolog-ical University,Singapore

School of Electrical and Electronic Engineering,Nanyang Technological University,Singapore

基金：

National Research Foundation,SingaporeSingapore and DSO National Laboratories under the AI Singapore ProgramRIE 2020 Advanced Manufacturing and Engineering(AME)Programmatic Fund SingaporeFuture Communications Research& Development Program

项目编号：

AISG2-RP-2020-019A20G8b0102FCP-NTU-RG-2021-014

出版年：

2024

DOI：

10.17775/CSEEJPES.2023.00230

中国电机工程学会电力与能源系统学报(英文版)

中国电机工程学会

中国电机工程学会电力与能源系统学报(英文版)

CSTPCDEI

ISSN：2096-0042

年,卷(期)：2024.10(1)

参考文献量12