A fast screening method for transient stability considering multi-swing step-out using pattern recognition with machine learning and clustering

Junnosuke Kobayashi, Yui Koyanagi, Shinichi Iwamoto

    Research output: Contribution to journalArticle

    Abstract

    Recently, online stability monitoring systems have become more important in response to the increasing complexity of power systems. Moreover, there has been a concern about multi-swing step-out due to the Japanese longitudinal power system. In this paper, a fast screening method is proposed considering multi-swing step-out using PCA (principal component analysis). In the proposed method, computers learn patterns of PCA in transient stability data as a form of library. In order to reduce the number of data in the library, k-means method, one of the partitioning-optimization clustering methods, is applied to extract features in the data. In addition, Gaussian mixture model is also applied to extract the feature from a different perspective. Simulations for the proposed method are performed using the IEEJ 10 machine 47 bus system to confirm the validity of the screening method.

    Original languageEnglish
    Pages (from-to)559-565
    Number of pages7
    JournalIEEJ Transactions on Power and Energy
    Volume137
    Issue number8
    DOIs
    Publication statusPublished - 2017

    Fingerprint

    Principal component analysis
    Pattern recognition
    Learning systems
    Screening
    Monitoring

    Keywords

    • Clustering
    • Multi-swing step-out
    • Pattern recognition
    • Power system
    • Principal component analysis
    • Transient stability

    ASJC Scopus subject areas

    • Energy Engineering and Power Technology
    • Electrical and Electronic Engineering

    Cite this

    A fast screening method for transient stability considering multi-swing step-out using pattern recognition with machine learning and clustering. / Kobayashi, Junnosuke; Koyanagi, Yui; Iwamoto, Shinichi.

    In: IEEJ Transactions on Power and Energy, Vol. 137, No. 8, 2017, p. 559-565.

    Research output: Contribution to journalArticle

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