A support vector machine (SVM) based voltage stability classifier

Rodel D. Dosano, Hwachang Song, Byongjun Lee

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

    Abstract

    This paper proposes a support vector machine (SVM) based power system voltage stability classifier using local measurements of voltage and active power of load. The excellent performance of the SVM in the classification related to time-series prediction matches the real-time data of local measurement for system responses by shortterm and long-term dynamics. The methodology for automatic monitoring of the system is initiated locally, which aims to leave sufficient time to perform immediate corrective actions to stop system degradation by the effect of major disturbances. This paper explains the procedure for fast classification of long-term voltage stability using the SVM algorithm.

    Original languageEnglish
    Title of host publicationProceedings of the 7th IASTED International Conference on Power and Energy Systems
    Pages265-271
    Number of pages7
    Publication statusPublished - 2007
    Event7th IASTED International Conference on Power and Energy Systems - Palma de Mallorca, Spain
    Duration: 2007 Aug 292007 Aug 31

    Publication series

    NameProceedings of the IASTED International Conference on Energy and Power Systems

    Other

    Other7th IASTED International Conference on Power and Energy Systems
    Country/TerritorySpain
    CityPalma de Mallorca
    Period07/8/2907/8/31

    Keywords

    • Classification
    • Local phasor measurement
    • Power system voltage stability
    • Real-time monitoring
    • Support vector machine

    ASJC Scopus subject areas

    • Energy Engineering and Power Technology
    • Electrical and Electronic Engineering
    • Condensed Matter Physics

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