BibTex RIS Cite

Detection of Power Disturbances using Emprical Mode Decomposition and Hilbert Transform

Year 2015, Volume: 2 Issue: 1, 27 - 34, 01.06.2016

Abstract

This paper presents a new method based emprical mode decomposition and hilbert transform for detection of power quality disturbances. Hilbert Huang Transform HHT, which was suggested by Huang and improved by Flandrin and his group, is a new signal processing method. The analysis of nonlinear and non stationary signals can use HHT. In this paper, the recorded disturbances signals are decomposed into Intrinsic Mode Functions using the Emprical Mode Decomposition. The frequency and amplitude of power disturbances are obtained of IMF components by using Hilbert Transform HT. The clear success of EMD in defining envelope variations of a sinusoidal waveform has been the main motivation for the adoption of EMD in analysis of power disturbance signals. Simulations are performed over waveforms including voltage harmonics, voltage sag and swell. The waveforms are selected as pure sinusoids. Simulation results show that the suggested methodology can effectively detect different power disturbances.

References

  • Dugan R.C., McGranaghan M.F., Santoso S., Beaty H.W., Electrical Power Systems Quality, MC Graw- Hill Companies, New York, 2003.
  • Gu Y., Bollen M. H. J., “Time-frequency and time-scale domain analysis of voltage disturbances,” IEEE Trans. Power Del., vol. 15, pp. 1279–1284, Oct. 2000.
  • Kwan T., Martin K., “Adaptive detection and enhancement of multiple sinusoids using a cascade of IIR filters,” IEEE Trans. Circuits Syst., vol. 36, pp. 936–947, Jul. 1989.
  • Flores R., Signal processing tools for power quality event classification, Lic.Eng. thesis, School Elect. Eng., Chalmers Univ. Technol.,Göteborg, Sweden, 2003.
  • Gaouda A. M., Kanoun S. H., Salama M. M. A., “On-line disturbance classification using nearest neighbor rule,” Int. J. Elect. Power Syst. Res., vol. 57, pp. 1–8, 2001.
  • Gu Y. H., Bollen M. H. J., “Time-frequency and time-scale domain analysis of voltage disturbances”, IEEE Trans. Power Del., vol. 15, pp. 1279–1284, Oct. 2000.
  • Poisson O., Rioual P., Meunier M., “Detection and measurement of power quality disturbances using wavelet transform,” IEEE Trans. Power Del., vol. 15, pp. 1039–1044, Jul. 2000.
  • Lin C.H., Wang C.H., “Adaptive wavelet networks for power-quality detection and discrimination in a power system”, IEEE Trans. Power Delivery, vol. 21, pp. 1106–1113, 2006.
  • Xu, J., Senroy, N., Suryanarayanan, S., & Ribeiro, P. “Some techniques for the analysis and visualization of time-varying waveform distortions”, In Power Symposium, NAPS 2006, 38th North American, pp. 257- 261, 2006
  • Chandrasekar P., Kamaraj V., “Detection and Classification of Power Quality Disturbance Waveform Using MRA Based Modified Wavelet Transform and Neural Networks”, Elect. Engineering, vol. 61, pp. 235–240, 2010.
  • Gaing Z. L., “Wavelet-based neural network for power disturbance recognition and classification”, IEEE Trans. Power Del., vol. 19, pp. 1560-1568, 2004.
  • Oleskovicz M., Coury D. V., Felho O. D., “Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks”, International Journal of Electrical Power & Energy Systems, Volume 31, Issue 5, pp. 206-212, June 2009.
  • Shukla S., Mishra S., Singh B., “Empirical mode decomposition with Hilbert transform for power-quality assesment”, IEEE Trans. Power Del., vol. 24, pp. 2159-2165, Oct. 2009.
  • Rilling G., Flandrin P., Gonvalves P., “On Emprical Mode Decomposition And İts Algorithms”, EEE- EURASIP Workshop on NSIP-03, Grado, vol. 3, pp. 8-11, 2003.
  • Huang N. E., Shen Z., Long S. R., et al. “The Emprical Mode Decomposition and The Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis”, Proc R. Soc. Lond A., vol. 454, pp. 903-995, 1998.
  • Onal Y., Ece D.G., Gerek O.N., “Analysis of voltage flicker using Hilbert-Huang Transform”, in Conf. SIU 19th, 2011, p.226-229.
Year 2015, Volume: 2 Issue: 1, 27 - 34, 01.06.2016

Abstract

References

  • Dugan R.C., McGranaghan M.F., Santoso S., Beaty H.W., Electrical Power Systems Quality, MC Graw- Hill Companies, New York, 2003.
  • Gu Y., Bollen M. H. J., “Time-frequency and time-scale domain analysis of voltage disturbances,” IEEE Trans. Power Del., vol. 15, pp. 1279–1284, Oct. 2000.
  • Kwan T., Martin K., “Adaptive detection and enhancement of multiple sinusoids using a cascade of IIR filters,” IEEE Trans. Circuits Syst., vol. 36, pp. 936–947, Jul. 1989.
  • Flores R., Signal processing tools for power quality event classification, Lic.Eng. thesis, School Elect. Eng., Chalmers Univ. Technol.,Göteborg, Sweden, 2003.
  • Gaouda A. M., Kanoun S. H., Salama M. M. A., “On-line disturbance classification using nearest neighbor rule,” Int. J. Elect. Power Syst. Res., vol. 57, pp. 1–8, 2001.
  • Gu Y. H., Bollen M. H. J., “Time-frequency and time-scale domain analysis of voltage disturbances”, IEEE Trans. Power Del., vol. 15, pp. 1279–1284, Oct. 2000.
  • Poisson O., Rioual P., Meunier M., “Detection and measurement of power quality disturbances using wavelet transform,” IEEE Trans. Power Del., vol. 15, pp. 1039–1044, Jul. 2000.
  • Lin C.H., Wang C.H., “Adaptive wavelet networks for power-quality detection and discrimination in a power system”, IEEE Trans. Power Delivery, vol. 21, pp. 1106–1113, 2006.
  • Xu, J., Senroy, N., Suryanarayanan, S., & Ribeiro, P. “Some techniques for the analysis and visualization of time-varying waveform distortions”, In Power Symposium, NAPS 2006, 38th North American, pp. 257- 261, 2006
  • Chandrasekar P., Kamaraj V., “Detection and Classification of Power Quality Disturbance Waveform Using MRA Based Modified Wavelet Transform and Neural Networks”, Elect. Engineering, vol. 61, pp. 235–240, 2010.
  • Gaing Z. L., “Wavelet-based neural network for power disturbance recognition and classification”, IEEE Trans. Power Del., vol. 19, pp. 1560-1568, 2004.
  • Oleskovicz M., Coury D. V., Felho O. D., “Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks”, International Journal of Electrical Power & Energy Systems, Volume 31, Issue 5, pp. 206-212, June 2009.
  • Shukla S., Mishra S., Singh B., “Empirical mode decomposition with Hilbert transform for power-quality assesment”, IEEE Trans. Power Del., vol. 24, pp. 2159-2165, Oct. 2009.
  • Rilling G., Flandrin P., Gonvalves P., “On Emprical Mode Decomposition And İts Algorithms”, EEE- EURASIP Workshop on NSIP-03, Grado, vol. 3, pp. 8-11, 2003.
  • Huang N. E., Shen Z., Long S. R., et al. “The Emprical Mode Decomposition and The Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis”, Proc R. Soc. Lond A., vol. 454, pp. 903-995, 1998.
  • Onal Y., Ece D.G., Gerek O.N., “Analysis of voltage flicker using Hilbert-Huang Transform”, in Conf. SIU 19th, 2011, p.226-229.

Details

Other ID JA48ZZ36ZN
Journal Section Articles
Authors

Yasemin ÖNAL This is me

Ömer Nezih GEREK This is me

Doğan Gökhan ECE This is me

Publication Date June 1, 2016
Submission Date July 1, 2016
Published in Issue Year 2015 Volume: 2 Issue: 1

Cite

APA ÖNAL, Y., GEREK, Ö. N., & ECE, D. G. (2016). Detection of Power Disturbances using Emprical Mode Decomposition and Hilbert Transform. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 2(1), 27-34.