Understanding discrepancy of power system dynamic security assessment with unknown faults: a reliable transfer learning-based method

Understanding discrepancy of power system dynamic security assessment with unknown faults: a reliable transfer learning-based method

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 i...

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Main Authors: Ren, Chao, Yu, Han, Xu, Yan, Dong, Zhao Yang
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2024
Subjects:
Engineering
Adversarial training
Dynamic security assessment
Online Access:https://hdl.handle.net/10356/174894
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1748942024-04-19T15:53:12Z Understanding discrepancy of power system dynamic security assessment with unknown faults: a reliable transfer learning-based method Ren, Chao Yu, Han Xu, Yan Dong, Zhao Yang School of Electrical and Electronic Engineering School of Computer Science and Engineering Engineering Adversarial training Dynamic security assessment 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. Nanyang Technological University National Research Foundation (NRF) Published version This work was 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; and Future Communications Research & Development Program (FCP-NTU-RG-2021-014). 2024-04-15T08:08:33Z 2024-04-15T08:08:33Z 2024 Journal Article Ren, C., Yu, H., Xu, Y. & Dong, Z. Y. (2024). Understanding discrepancy of power system dynamic security assessment with unknown faults: a reliable transfer learning-based method. CSEE Journal of Power and Energy Systems, 10(1), 427-431. https://dx.doi.org/10.17775/CSEEJPES.2023.00230 2096-0042 https://hdl.handle.net/10356/174894 10.17775/CSEEJPES.2023.00230 2-s2.0-85185201359 1 10 427 431 en AISG2-RP-2020-019 A20G8b0102 FCP-NTU-RG-2021-014 CSEE Journal of Power and Energy Systems © 2023 CSEE. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Adversarial training
Dynamic security assessment
spellingShingle Engineering
Adversarial training
Dynamic security assessment
Ren, Chao
Yu, Han
Xu, Yan
Dong, Zhao Yang
Understanding discrepancy of power system dynamic security assessment with unknown faults: a reliable transfer learning-based method
description 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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Ren, Chao
Yu, Han
Xu, Yan
Dong, Zhao Yang
format Article
author Ren, Chao
Yu, Han
Xu, Yan
Dong, Zhao Yang
author_sort Ren, Chao
title Understanding discrepancy of power system dynamic security assessment with unknown faults: a reliable transfer learning-based method
title_short Understanding discrepancy of power system dynamic security assessment with unknown faults: a reliable transfer learning-based method
title_full Understanding discrepancy of power system dynamic security assessment with unknown faults: a reliable transfer learning-based method
title_fullStr Understanding discrepancy of power system dynamic security assessment with unknown faults: a reliable transfer learning-based method
title_full_unstemmed Understanding discrepancy of power system dynamic security assessment with unknown faults: a reliable transfer learning-based method
title_sort understanding discrepancy of power system dynamic security assessment with unknown faults: a reliable transfer learning-based method
publishDate 2024
url https://hdl.handle.net/10356/174894
_version_ 1806059818246471680

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