Power Quality Event Classification Using Hilbert Huang Transform

  • R. Jalaja
  • B. Biswal
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)


The objective of this paper is to develop a method based on combination of empirical-mode decomposition (EMD) and Hilbert transform for power quality events classification. Non-stationary power signal disturbance waveform can be considered as superimposition of various undulating modes and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMF). Hilbert transform is applied to all the IMF to achieve instantaneous amplitude and frequency. Relevant feature vectors are extracted to do the automatic classification. Time frequency analysis shows clear visual detection, localization and classification of the different power signal disturbances. A balanced neural tree is used to classify the power signal patterns.


Non-Stationary power signals EMD (Empirical Mode Decomposition) Hilbert Transform Balanced Neural Tree 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • R. Jalaja
    • 1
  • B. Biswal
    • 2
  1. 1.ECE BranchGMRITRajamIndia
  2. 2.ECE Dept.GMRITRajamIndia

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