理解未知故障下电力系统动态安全评估的差异：基于迁移学习的可靠方法

IF 6.9 2区工程技术 Q2 ENERGY & FUELS

CSEE Journal of Power and Energy Systems Pub Date : 2023-09-08 DOI:10.17775/CSEEJPES.2023.00230

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

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引用次数: 0

摘要

本文提出了一种用于电力系统故障前动态安全评估（DSA）的可靠迁移学习（RTL）方法，以提高 DSA 在潜在相关未知故障情况下的性能。该方法将个体差异考虑在内，可处理数据不完整的未知故障。大量实验结果表明，所提出的 RTL 方法具有较高的 DSA 精度和计算效率。理论分析表明 RTL 可以保证系统性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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 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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来源期刊

CSEE Journal of Power and Energy Systems Energy-Energy (all)

CiteScore

11.80

自引率

12.70%

发文量

389

审稿时长

26 weeks

期刊介绍： The CSEE Journal of Power and Energy Systems (JPES) is an international bimonthly journal published by the Chinese Society for Electrical Engineering (CSEE) in collaboration with CEPRI (China Electric Power Research Institute) and IEEE (The Institute of Electrical and Electronics Engineers) Inc. Indexed by SCI, Scopus, INSPEC, CSAD (Chinese Science Abstracts Database), DOAJ, and ProQuest, it serves as a platform for reporting cutting-edge theories, methods, technologies, and applications shaping the development of power systems in energy transition. The journal offers authors an international platform to enhance the reach and impact of their contributions.