Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Hilbert Transform based Fuzzy Expert System for Diagnosing and Classifying Power Quality Disturbances

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
P. Kalyana Sundaram, R. Neela
10.5120/ijca2016909729

Kalyana P Sundaram and R Neela. Hilbert Transform based Fuzzy Expert System for Diagnosing and Classifying Power Quality Disturbances. International Journal of Computer Applications 142(3):48-55, May 2016. BibTeX

@article{10.5120/ijca2016909729,
	author = {P. Kalyana Sundaram and R. Neela},
	title = {Hilbert Transform based Fuzzy Expert System for Diagnosing and Classifying Power Quality Disturbances},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2016},
	volume = {142},
	number = {3},
	month = {May},
	year = {2016},
	issn = {0975-8887},
	pages = {48-55},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume142/number3/24880-2016909729},
	doi = {10.5120/ijca2016909729},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

This paper presents a new technique for diagnosis and classification of power quality disturbances. The proposed method applies Hilbert transform to analyze the distorted voltage waveforms and then extract their features. The distorted voltage waveforms are generated by Matlab simulink on the test system. The extracted input features such as standard deviation and variances are given as inputs to the fuzzy-expert system that uses some rules to classify the Power Quality disturbances. Fuzzy classifier has been constructed to classify both the single and combined form power quality disturbances. The results clearly show that the proposed method has the ability to diagnosize and classify Power Quality problems. The results obtained by the proposed method are validated by comparing them against Hilbert Transform based neural classifiers.

References

  1. Shyh-Jier Huang , Cheng-Tao Hsieh and Ching-Lien Huang, “Application of wavelets to classify power system disturbances”, Electric power systems research,1998.
  2. G.T.Heydt, P.S.Fjeld, C.C.Liu, D.Pierce, L.Tu,G.Hensley, “Applications of the windowed FFT to Electric Power quality assessment”, IEEE Transaction on power delivery, October 1999.
  3. A.M. Gaouda, M.R.Sultan, A.Y.Chinkani and M.M.A. Salama, “Power quality detection and classification using wavelet-multiresolution signal decomposition” IEEE Transaction on power delivery, Vol 14, no.4, October 1999.
  4. A.M. Gaouda, S.H. Kanoun, M.M.A. Salama, “On-line disturbance classification using nearest neighbor rule”, Electric power systems research, 2001.
  5. A.Elmitwally, S.Farghal, M.Kandil, S.Abdelkader, and M.Elkateb, “Proposed wavelet-neuro fuzzy combined system for power quality violations detection and diagnosis”, IEEE Transaction, 2001.
  6. Min Wang, Piotr Ochenkowski, and Alexander Mamishev, “Classification of power quality disturbances using time-frequency ambiguity plane and neural networks”, IEEE Transaction, 2001.
  7. Francisco Jurado , Jose´ R. Saenz, “Comparison between discrete STFT and wavelets for the analysis of power quality events”, Electric power systems research, 2002.
  8. P.K. Dash, B.K. Panigrahi, G. Panda, “Power quality analysis using S-transform”, IEEE Transaction on power delivery, Vol 18, no.2, April 2003.
  9. P.K Dash, I. W. C. Lee, “S-transform-based intelligent system for classification of power quality disturbance signals”, IEEE Transaction on industrial electronics, Vol 50, no.4, August 2003.
  10. P.K. Dash, B.K. Panigrahi, G. Panda, and D.K.Sahoo, “Power quality disturbance data compression, detection and classification using integrated spline wavelet and S-transform”, IEEE Transaction on power delivery, Vol 18, no.2, April 2003.
  11. M. V. Chilukuri and P. K. Dash, “Multi resolution S-transform-based fuzzy recognition system for power quality events”, IEEE Transaction on power delivery, Vol 19, no.1, January 2004.
  12. Dogan gokhan ece, and Omer nezih gerek, “Power quality event detection using joint 2-D-wavelet subspaces”, IEEE Transaction on instrumentation and measurement, Vol 53, no.4, August 2004.
  13. A. K. Pradhan, A. Routray, A. Behera, “Power quality disturbance classification employing modular wavelet network”, IEEE Transactions, 2006.
  14. Haibo He, Janusz A.Starzyk, A self organizing learning array system for power quality classification based on wavelet transform , IEEE Transaction on power delivery, Vol 21, no.1, January 2006.
  15. Peter G. V. Axelberg, Irene Yu-Hua Gu and Math H. J. Bollen, “Support vector machine for classification of voltage disturbances”, IEEE Transactions on power delivery, July 2007.
  16. S. Mishra, C. N. Bhende, and B. K. Panigrahi, “Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network”, IEEE Trans. Power Delivery. Vol.23, 2008.
  17. Chun-Yao Lee, Yi-Xing Shen, “Optimal Feature Selection for Power-Quality Disturbances Classification”, IEEE Transaction on power delivery, oct 2011.
  18. Abdelazeem A.Abdelsalam, Azza A.Eldesouky, Abdelhay A.Sallam, "Characterization of power quality disturbance using hybrid technique of linear kalman filter and fuzzy expert system, Electric power systems research,2012.
  19. B. Biswal, M. Biswal, S. Mishra and R. Jalaja, “Automatic Classification of Power Quality Events Using Balanced Neural Tree”, IEEE Transaction on Industrial electronics. Vol 61, January 2014.
  20. M.Sabarimalai manikandan, R.Samantary, Innocent Kamwa, Jan 2015 “Detection and classification of Power quality disturbances using sparse signal decomposition on hybrid dictionaries”, IEEE Transactions on Instrument and measurement, Vol 64, No.1, Jan 2015.

Keywords

Power quality, Power quality disturbances, Hilbert transforms, Fuzzy-expert system.