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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Bibliographic Details
Main Authors: Chao Ren, Han Yu, Yan Xu, Zhao Yang Dong
Format: Article
Language:English
Published: China electric power research institute 2024-01-01
Series:CSEE Journal of Power and Energy Systems
Subjects:
Adversarial training
dynamic security assessment
maximum classifier discrepancy
missing data
transfer learning
Online Access:https://ieeexplore.ieee.org/document/10246181/
Description
Summary: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.
ISSN:2096-0042

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