Time Frequency Analysis and Classification of Power Quality Events Using Bacteria Foraging Algorithm

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)

Abstract

This paper proposes a novel method Modified Hilbert Huang Transform which is the combination of empirical-mode decomposition (EMD) and Hilbert transform with an equivalent window for Time frequency analysis. Initially the Non-stationary power signal is decomposed using EMD to get Intrinsic Mode functions (IMFs) and then Hilbert transform with an equivalent window is applied to all the IMFs to obtain instantaneous amplitude and frequency for Modified Hilbert Energy Spectrum. Different features are extracted from the Modified Hilbert Energy Spectrum and these features are applied to the Bacteria Foraging algorithm for automatic classification.

Keywords

Non-stationary power signals EMD (Empirical mode decomposition) Modified Hilbert Huang transform (MHHT) Bacteria foraging algorithm 

References

  1. 1.
    Sejdic, E., Djurovic, E., Jiang, J.: Time–frequency feature representation using energy concentration: an overview of recent advances. Digit. Signal Proc. 19, 153–183 (2009)CrossRefGoogle Scholar
  2. 2.
    Edward Reid, W.: Power quality issues-standards and guidelines. IEEE Trans. Ind. Appl. 32(3) (1996)Google Scholar
  3. 3.
    Biswal, B., Dash, P.K., Panigrahi, B.K. : Power quality disturbance classification using fuzzy C-means algorithm and adaptive particle swarm optimization. IEEE Trans. Ind. Electron. 56(1) (2009)Google Scholar
  4. 4.
    Santoso, S., Grady, W.M., Powers, E.: Characterization of distribution power quality events with fourier and wavelet transforms. IEEE Trans. Power Deliv. 15(1), 247–254 (2000)Google Scholar
  5. 5.
    Biswal, B., Mishra, S.: Detection and classification of disturbances in non-stationary signals using modified frequency slice wavelet transform. Gen. Trans. Distrib. (IET) 7(9) (2013)Google Scholar
  6. 6.
    Jayasree, T., Devaraj, D., Sukanesh, R.: Power quality disturbance classification using hilbert transform and RBF networks. Neurocomputing 73, 1451–1456(2010)Google Scholar
  7. 7.
    Biswal, B., Biswal, M., Mishra, S., Jalaja, R.: Automatic classification of power quality disturbances with balanced neural tree. IEEE Trans. Ind. Electron. 61(1) (2014)Google Scholar
  8. 8.
    Sun, S., Jiang, Z., Wang, H., Yu, F.: Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform. Comput. Methods Programs Biomed. 114(3), 219–230 (2014)CrossRefGoogle Scholar
  9. 9.
    Mishra, S.: A hybrid least square-fuzzy bacteria foraging strategy for harmonic estimation. IEEE Trans. Evol. Comput. 9(1), 61–73 (2005)CrossRefGoogle Scholar
  10. 10.
    Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22(3), 52–67 (2002)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of ECEGMR Institute of TechnologySrikakulam DistrictIndia

Personalised recommendations