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


    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
    Issue number8
    Publication statusPublished - 2017



    • 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

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