• DocumentCode
    2447116
  • Title

    Detection of blackouts by using K-means clustering in a power system

  • Author

    Ozgonenel, Okan ; Thomas, David W. P. ; Yalcin, Tolga ; Bertizlioglu, Ismail N.

  • Author_Institution
    Ondokuz Mayis University, Electrical & Electronic Engineering Faculty, 55139, Kurupelit, Samsun, Turkey
  • fYear
    2012
  • fDate
    23-26 April 2012
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    This paper presents a novel approach for the detection of abnormal power system states that force systems into blackout. K-means clustering techniques and two types of distances for identifying pattern clusters are used to detect abnormal conditions. PCA is used for the reduction of the data matrix for faster calculations. The proposed hybrid technique is then demonstrated on an IEEE 14-bus system.
  • Keywords
    Clustering algorithms; Covariance matrix; Eigenvalues and eigenfunctions; Generators; Load flow; Power system stability; Principal component analysis; Anomaly (Outlier) Detection; Blackout; K-Means Clustering; Principal Component Analysis (PCA);
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Developments in Power Systems Protection, 2012. DPSP 2012. 11th International Conference on
  • Conference_Location
    Birmingham, UK
  • Print_ISBN
    978-1-84919-620-8
  • Type

    conf

  • DOI
    10.1049/cp.2012.0079
  • Filename
    6227567
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2447116