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

Chao Ren; Han Yu; Yan Xu; Zhao Yang Dong

doi:10.17775/CSEEJPES.2023.00230

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

Regular Papers | 更新时间：2026-10-15

- 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
- 中国电机工程学会电力与能源系统学报(英文) 2024年10卷第1期页码：427-431
- 作者机构：
  
  1. School of Computer Science and Engineering, Nanyang Technological University,Singapore
  2. School of Electrical and Electronic Engineering, Nanyang Technological University,Singapore,639798
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2023.00230
  中图分类号：
- 纸质出版：2024-01-01
- 稿件说明：
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Chao Ren, Han Yu, Yan Xu, 等. Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(1):427-431.

Chao Ren, Han Yu, Yan Xu, et al. Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method[J]. CSEE Journal of Power and Energy Systems, 2024, 10(1): 427-431.
Chao Ren, Han Yu, Yan Xu, 等. Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method[J]. 中国电机工程学会电力与能源系统学报(英文), 2024,10(1):427-431. DOI： 10.17775/CSEEJPES.2023.00230.

Chao Ren, Han Yu, Yan Xu, et al. Understanding Discrepancy of Power System Dynamic Security Assessment with Unknown Faults: A Reliable Transfer Learning-based Method[J]. CSEE Journal of Power and Energy Systems, 2024, 10(1): 427-431. DOI： 10.17775/CSEEJPES.2023.00230.

摘要

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 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.

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