Transient stability multi-swing step-out prediction with online anomaly detection

Takuya Omi, Hiroto Kakisaka, Tomomi Sadakawa, Shinichi Iwamoto

    研究成果: Conference contribution

    抄録

    Electric power systems are becoming more complex making them more difficult to control. Owing to recent developments in information and communication technologies, power system data have become available online. In this paper, we propose a method that can predict transient stability multi-swing step-out using 'anomaly detection with data mining'. In particular, we focus our attention on the theory of ChangeFinder, which uses the SDAR algorithm and the two-stage learning model. The generator phase angles are obtained from transient stability simulations. They are passed as inputs to the ChangeFinder and the multi swing step-out can be detected. The validity of the proposed method is verified through simulations on the IEEJ 10 machine 47 bus-system.

    本文言語English
    ホスト出版物のタイトルProceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ3123-3126
    ページ数4
    ISBN(電子版)9781509025961
    DOI
    出版ステータスPublished - 2017 2 8
    イベント2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore
    継続期間: 2016 11 222016 11 25

    Other

    Other2016 IEEE Region 10 Conference, TENCON 2016
    国/地域Singapore
    CitySingapore
    Period16/11/2216/11/25

    ASJC Scopus subject areas

    • コンピュータ サイエンスの応用
    • 電子工学および電気工学

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