Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based MethodOACSTPCDEI
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 discrepancies 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.
Chao Ren;Han Yu;Yan Xu;Zhao Yang Dong;
School of Computer Science and Engineering,Nanyang Technological University,SingaporeSchool of Electrical and Electronic Engineering,Nanyang Technological University,Singapore
动力与电气工程
Adversarial trainingdynamic security assessmentmaximum classifier discrepancymissing datatransfer learning
《CSEE Journal of Power and Energy Systems》 2024 (001)
P.427-431 / 5
supported by the Internal Talent Award(TRACS)with Wallenberg-NTU Presidential Postdoctoral Fellowship 2022;the National Research Foundation,Singapore and DSO National Laboratories under the AI Singapore Program(AISG Award No:AISG2-RP-2020-019);the RIE 2020 Advanced Manufacturing and Engineering(AME)Programmatic Fund(No.A20G8b0102),Singapore;Future Communications Research&Development Program(FCP-NTU-RG-2021-014).
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