• DocumentCode
    3398659
  • Title

    The Swing-Blocking Methods for Digital Distance Protection Based on Wavelet Packet Transform and Support Vector Machine

  • Author

    Kampeerawat, W. ; Buangam, W. ; Chusanapiputt, S.

  • Author_Institution
    Dept. of Electr. Power Eng., Mahanakorn Univ. of Technol., Bangkok, Thailand
  • fYear
    2012
  • fDate
    27-29 March 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    This paper presents a method for power swing and fault diagnosis of power system based on Wavelet Packet Transform(WPT) and Support Vector Machine (SVM) classifier. The method adopts Least Square Support Vector Machine (LS-SVM) classifier to identify the power swing and fault types. The power swing blocking elements are based on monitoring the rate of change of wavelet packet energy and wavelet packet entropy of voltage and current signal, the positive current and zero sequence component. The process of training the LS-SVM using a K-folded cross validation process for determining the values of parameter σ and parameter γ in RBF kernel parameters can minimize the classification error. The proposed method can successfully detect power swing and provide power swing blocking signal for accurate distance protection.
  • Keywords
    entropy; fault diagnosis; learning (artificial intelligence); least squares approximations; pattern classification; power engineering computing; power system faults; power system protection; radial basis function networks; support vector machines; wavelet transforms; K-folded cross validation process; LS-SVM classifier; RBF kernel parameter; WPT; classification error minimization; current signal; digital distance protection; least square support vector machine classifier; positive current; power swing blocking signal detection; power swing identification; power system fault diagnosis; swing-blocking method; voltage signal; wavelet packet energy; wavelet packet entropy; wavelet packet transform; zero sequence component; Entropy; Power systems; Protective relaying; Support vector machines; Training; Wavelet packets;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Engineering Conference (APPEEC), 2012 Asia-Pacific
  • Conference_Location
    Shanghai
  • ISSN
    2157-4839
  • Print_ISBN
    978-1-4577-0545-8
  • Type

    conf

  • DOI
    10.1109/APPEEC.2012.6307628
  • Filename
    6307628