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

Takuya Omi, Hiroto Kakisaka, Tomomi Sadakawa, Shinichi Iwamoto

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    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.

    Original languageEnglish
    Title of host publicationProceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages4
    ISBN (Electronic)9781509025961
    Publication statusPublished - 2017 Feb 8
    Event2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore
    Duration: 2016 Nov 222016 Nov 25


    Other2016 IEEE Region 10 Conference, TENCON 2016


    • Anomaly Detection
    • Data Mining
    • Multi-Swing Step-out
    • Online Analysis
    • Transient Stability

    ASJC Scopus subject areas

    • Computer Science Applications
    • Electrical and Electronic Engineering


    Dive into the research topics of 'Transient stability multi-swing step-out prediction with online anomaly detection'. Together they form a unique fingerprint.

    Cite this