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 ind...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.展开更多
基金supported by the Internal Talent Award(TRACS)with Wallenberg-NTU Presidential Postdoctoral Fellowship 2022the National Research Foundation,Singapore and DSO National Laboratories under the AI Singapore Program(AISG Award No:AISG2-RP-2020-019)+1 种基金the RIE 2020 Advanced Manufacturing and Engineering(AME)Programmatic Fund(No.A20G8b0102),SingaporeFuture Communications Research&Development Program(FCP-NTU-RG-2021-014).
文摘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.
