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Regular Paper | Open Access

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

Chao Ren1, Han Yu1( ), Yan Xu2, Zhao Yang Dong2
School of Computer Science and Engineering, Nanyang Technological University, Singapore
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
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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 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.

Keywords

Adversarial trainingdynamic security assessmentmaximum classifier discrepancymissing datatransfer learning

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CSEE Journal of Power and Energy Systems
Volume 10 Issue 1,
January 2024
Pages 427-431
DOI: 10.17775/CSEEJPES.2023.00230
Cite this article:
Ren C, Yu H, Xu Y, et al. Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method. CSEE Journal of Power and Energy Systems, 2024, 10(1): 427-431. https://doi.org/10.17775/CSEEJPES.2023.00230

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Received: 16 January 2023
Revised: 18 May 2023
Accepted: 20 June 2023
Published: 08 September 2023
© 2023 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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