ISSN 1000-1026
CN 32-1180/TP
  • ISSN 1000-1026
  • CN 32-1180/TP

Citation: XUE Yusheng,HUANG Tiangang,CHEN Guoping,ZHENG Yuping,WEN Fushuan,XU Yan,ZHAO Junhua.Review on Case Filtering in Transient Stability Analysis[J].Automation of Electric Power Systems,2019,43(6):1-14. DOI: 10.7500/AEPS20181008004 copy

Review on Case Filtering in Transient Stability Analysis

  • Received Date: October 08, 2018
    Accepted Date: November 19, 2018
    Available Online: February 19, 2019

  • Abstract:

        In the case filtering link, as many stable and unstable cases as possible can be identified rapidly through qualitative machine learning or quantitative approximate analysis so that the number of cases requiring detailed analysis and the total computational burden can be reduced. This paper discusses the adopted assumptions, characteristic variables, classification rules and generalization ability of case filtering in transient stability analysis. The integration of data driven and model driven is analyzed, which includes the introduction of causal elements, the deep integration of data statistic paradigm and model simulation paradigm for knowledge extraction. Based on the stability mechanism, a two-layer classifier is proposed: the lower layer contains several parallel links, and each link utilizes quantitative algorithm with different degrees of approximation. Their output data are regarded as the input of the upper layer. According to the approximate causality, it can reflect the influence of most original data on transient stability. Therefore not only the correct recognition rate and robustness of the classifier can be both enhanced, but also the error mechanism, evaluation credibility and acceptability can be revealed.

  • Keywords:

    theoretical analysis; model simulation; statistical analysis; case filtering; machine learning; multi-classifier; error analysis

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