CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Classification of Voltage Sag Causes using Probabilistic Neural Network and Hilbert ñ Huang Transform

by M. Manjula, A.V.R.S. Sarma
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 20
Year of Publication: 2010
Authors: M. Manjula, A.V.R.S. Sarma
10.5120/427-630

M. Manjula, A.V.R.S. Sarma . Classification of Voltage Sag Causes using Probabilistic Neural Network and Hilbert ñ Huang Transform. International Journal of Computer Applications. 1, 20 ( February 2010), 22-29. DOI=10.5120/427-630

@article{ 10.5120/427-630,
author = { M. Manjula, A.V.R.S. Sarma },
title = { Classification of Voltage Sag Causes using Probabilistic Neural Network and Hilbert ñ Huang Transform },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 20 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 22-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number20/427-630/ },
doi = { 10.5120/427-630 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:47:06.699737+05:30
%A M. Manjula
%A A.V.R.S. Sarma
%T Classification of Voltage Sag Causes using Probabilistic Neural Network and Hilbert ñ Huang Transform
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 20
%P 22-29
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Any power quality disturbance waveform can be seen as superimposition of various oscillating modes. It becomes necessary to separate different components of single frequency or narrow band of frequencies from a non stationary signal to identify the causes which contribute to power quality disturbances. In this paper a method is proposed to detect and classify voltage sag causes based on Empirical Mode Decomposition (EMD) with Hilbert Transform ( called Hilbert-Huang Transform) and Probabilistic Neural Network (PNN). The key feature of EMD is to decompose a non stationary signal into mono component signals called Intrinsic Mode Functions (IMFs). Further the Hilbert transform of each IMF provides frequency information evolving with time and variation in magnitude and phase due to oscillation at different time scales and locations. The characteristic features of the first three IMFs of each disturbance waveform are obtained. Finally PNN is used to classify the characteristic features for identification of voltage sag causes. Three voltage sag causes are taken for classification (i) Three phase short circuit (ii) Starting of induction motor and (iii) Three phase transformer energization. Results show that the classifier can detect and classify the voltage sag causes efficiently.

References
  1. Damarla, G. P., Chandrasekaran, A., & Sundaram, A. “Classification of power system disturbances through fuzzy neural network” in Electrical and Computer Engineering conference (pp. 68–71). Canada.owman, M., Debray, S. K., and Peterson, L. L. 1993.
  2. R. A. Flores, “State of art in the classification of power quality events, an overview,”, in Proc. 10th Int. Conf. Harmonics Quality Power,2002, vol. 1,pp.17-20.
  3. Y. H. Gu and M. H. J. Bollen, “ Time-frequency and time-scale domain analysis of voltage disturbances,” IEEE Trans. Power Delivery, vol. 15, no.4 pp. 1279-1284, October 2000.
  4. F. Jurado, N. Acero, and B. Ogayar, “Application of signal processing tools for power quality ,” in Proc. Canadian Conf. Electrical and Computer Engineering ,May 2002, vol.1,pp 82-87.
  5. S. Santoso, W. M. Grady, E. J. Powers, J. Lamoure, and S. C. Bhatt, “Characterization of distribution power quality events with Fourier and Wavelet transforms,”IEEE Trans. Power Delivery , vol.15, no.1,pp. 247-245,January 2000.
  6. Z. L. Gaing , “ Wavelet based neural network for power disturbance recognition and classification,” IEEE Trans. Power Delivery , vol.19, no.4,pp.1560-1568,Oct. 2004.
  7. M.Gaouda, M.M.A.Salama and M.R.Sultan, A.Y.Chikhani, "Power Quality Detection and Classification Using Wavelet Multiressolution Signal Decomposition”, IEEE Transactions on Power Delivery, Volume 14, Issue 4 October 1999, pp. 1469-1476.
  8. M. V. Chiukuri and P. K. Dash, “Multiresolution S-Transform based fuzzy recognition system for power quality events,” IEEE Trans. Power Delivery ,vol.19,no.1, pp.323-330,January 2004.
  9. N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung, H.H. Liu, The empirical mode decomposition and hilbert spectrum for nonlinear and nonstationary time series analysis, Proceedings of the Royal Society, London, Series A 454, 903–995, 1998.
  10. H. Amaris, C. Alvarez, M. Alonso, D. Florez, T. Lobos, P. Janik, J. Rezmer, Z. Waclawek,” Application of advanced signal processing methods for accurate detection of voltage dips,’ 13th International Conference on Harmonics and Quality of Power, ICHQP 2008,Wollongong,Australia, pp.6 28th September 2008.
  11. S. Mishra, C. N. Bhende, and B. K. Panigrahi, “ Detection and classification of power quality using S-transforms and probabilistic neural network, “ IEEE Transactions on Power Delivery, Vol 23, Issue 1, January 2008, pp.280-287.284.
Index Terms

Computer Science
Information Sciences

Keywords

Empirical mode decomposition intrinsic mode functions hilbert transform probabilistic neural network

Powered by PhDFocusTM