• DocumentCode
    2157294
  • Title

    Feature vector extraction by using empirical mode decomposition from power quality disturbances

  • Author

    Yalcin, Tolga ; Ozgonenel, Okan

  • Author_Institution
    Electr. & Electron. Eng. Dept., Ondokuz Mayis Univ., Kurupelit - Samsun, Turkey
  • fYear
    2012
  • fDate
    18-20 April 2012
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    Power quality assumes that voltages/currents are in rated frequency and values from transmission to distribution and their shape is pure sinusoid and except these comments the distributed electrical energy is assumed as `not in good quality´. In this paper, the method known as empirical mode decomposition (EMD) will be used for extracting feature vectors from distorted power signal. The proposed method uses three phase normalized voltage/current signals but single phase analysis of voltage signals will be implemented in this work. Power disturbances such as voltage sag, swell, interrupt, flicker and DC component analysis are successfully decomposed to obtain feature vectors for any classification algorithm by using EMD.
  • Keywords
    feature extraction; power distribution; signal classification; signal detection; DC component analysis; EMD; classification algorithm; distorted power signal; distributed electrical energy; empirical mode decomposition; feature vector extraction; phase normalized current signals; phase normalized voltage signals; power distribution; power disturbances; power quality disturbances; power transmission; single phase analysis; voltage sag; Feature extraction; Monitoring; Power quality; Real time systems; Signal processing; Vectors; Voltage fluctuations;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Communications Applications Conference (SIU), 2012 20th
  • Conference_Location
    Mugla
  • Print_ISBN
    978-1-4673-0055-1
  • Electronic_ISBN
    978-1-4673-0054-4
  • Type

    conf

  • DOI
    10.1109/SIU.2012.6204434
  • Filename
    6204434