Skip to main content
SpringerLink
Log in

A New S-Transform-Based Method for Identification of Power Quality Disturbances

  • Research Article - Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Nowadays, the identification and mitigation of power quality (PQ) disturbances are very important issues for electric companies and industries. In this paper, a new method is presented for the classification of PQ disturbances. At first, the proposed characteristics are extracted from the waveforms of the disturbances using S-transform (ST). Then, based on the values of these characteristics, the types of the disturbances are determined. This method is evaluated under noisy and noiseless conditions on a database of 7000 simulated waveforms describing fourteen types of PQ disturbances, which include impulse, interruption, swell, sag, notch, oscillatory transient, harmonic, and flicker as single disturbances with their six possible combinations. The results of this study demonstrate the acceptable accuracy of the proposed method in identifying the disturbances in addition to its very low sensitivity under various noisy conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhang, M.; Li, K.; Hu, Y.: A real-time classification method of power quality disturbances. Electr. Power Syst. Res. 81, 660–666 (2011)

    Article  Google Scholar 

  2. Reddy, M.J.B.; Raghupathy, R.K.; Venkatesh, K.P.; Mohanta, D.K.: Power quality analysis using discrete orthogonal S-transform (DOST). Digit. Signal Proc. 23, 616–626 (2013)

    Article  MathSciNet  Google Scholar 

  3. Nguyen, T.; Liao, Y.: Power quality disturbance classification utilizing S-transform and binary feature matrix method. Electr. Power Syst. Res. 79, 569–575 (2009)

    Article  Google Scholar 

  4. Moravej, Z.; Abdoos, A.A.; Pazoki, M.: New combined S-transform and logistic model tree technique for recognition and classification of power quality disturbances. Electr. Power Compon. Syst. 39, 80–98 (2011)

    Article  Google Scholar 

  5. Gargoom, A.M.; Ertugrul, N.; Soong, W.L.: Automatic classification and characterization of power quality events. IEEE Trans. Power Deliv. 23, 2417–2425 (2008)

    Article  Google Scholar 

  6. Liao, Y.; Lee, J.B.: A fuzzy-expert system for classifying power quality disturbances. Int. J. Electr. Power Energy Syst. 26, 199–205 (2004)

    Article  Google Scholar 

  7. Hooshmand, R.; Enshaee, A.: Detection and classification of single and combined power quality disturbances using fuzzy systems oriented by particle swarm optimization algorithm. Electr. Power Syst. Res. 80, 1552–1561 (2010)

    Article  Google Scholar 

  8. Abdel-Galil, T.K.; El-Saadany, E.F.; Youssef, A.M.; Salama, M.M.A.: Disturbance classification using hidden Markov models and vector quantization. IEEE Trans. Power Deliv. 20, 2129–2135 (2005)

    Article  Google Scholar 

  9. Barros, J.; de Apraiz, M.; Diego, R.I.: A virtual measurement instrument for electrical power quality analysis using wavelets. Measurement 42, 298–307 (2009)

    Article  Google Scholar 

  10. Panigrahi, B.K.; Pandi, V.R.: Optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy \(k\)-nearest neighbour algorithm. IET Gener. Transm. Distrib. 3, 296–306 (2009)

    Article  Google Scholar 

  11. Liao, C.C.; Yang, H.T.: Recognizing noise-influenced power quality events with integrated feature extraction and neuro-fuzzy network. IEEE Trans. Power Deliv. 24, 2132–2141 (2009)

    Article  Google Scholar 

  12. Reaz, M.B.I.; Choong, F.; Mohd-Yasin, F.: VHDL modeling for classification of power quality disturbance employing wavelet transform, artificial neural network and fuzzy logic. Simulation 82, 867–881 (2006)

    Article  Google Scholar 

  13. Decanini, J.G.M.S.; Tonelli-Neto, M.S.; Malange, F.C.V.; Minussi, C.R.: Detection and classification of voltage disturbances using a fuzzy-ARTMAP-wavelet network. Electr. Power Syst. Res. 81, 2057–2065 (2011)

    Article  Google Scholar 

  14. Kaewarsa, S.; Attakitmongcol, K.; Kulworawanichpong, T.: Recognition of power quality events by using multiwavelet-based neural networks. Int. J. Electr. Power Energy Syst. 30, 254–260 (2008)

    Article  Google Scholar 

  15. Uyar, M.; Yildirima, S.; Gencoglu, M.T.: An effective wavelet-based feature extraction method for classification of power quality disturbance signals. Electr. Power Syst. Res. 78, 1747–1755 (2008)

    Article  Google Scholar 

  16. He, H.; Starzyk, J.A.: A self-organizing learning array system for power quality classification based on wavelet transform. IEEE Trans. Power Deliv. 21, 286–295 (2006)

    Article  Google Scholar 

  17. Masoum, M.A.S.; Jamali, S.; Ghaffarzadeh, N.: Detection and classification of power quality disturbances using discrete wavelet transform and wavelet networks. IET Sci. Meas. Technol. 4, 193–205 (2010)

    Article  Google Scholar 

  18. Moravej, Z.; Pazoki, M.; Abdoos, A.A.: Wavelet transform and multi-class relevance vector machines based recognition and classification of power quality disturbances. Eur. Trans. Electr. Power 21, 212–222 (2011)

    Article  Google Scholar 

  19. Eristi, H.; Ucar, A.; Demir, Y.: Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines. Electr. Power Syst. Res. 80, 743–752 (2010)

    Article  Google Scholar 

  20. Ekici, S.: Classification of power system disturbances using support vector machines. Expert Syst. Appl. 36, 9859–9868 (2009)

    Article  Google Scholar 

  21. Hu, G.S.; Zhu, F.F.; Ren, Z.: Power quality disturbance identification using wavelet packet energy entropy and weighted support vector machines. Expert Syst. Appl. 35, 143–149 (2008)

    Article  Google Scholar 

  22. Kapoor, R.; Saini, M.K.: Multiwavelet transform based classification of PQ events. Eur. Trans. Electr. Power 22, 518–532 (2012)

    Article  Google Scholar 

  23. He, Z.; Zhang, H.; Zhao, J.; Qian, Q.: Classification of power quality disturbances using quantum neural network and DS evidence fusion. Eur. Trans. Electr. Power 22, 533–547 (2012)

    Article  Google Scholar 

  24. Biswal, M.; Dash, P.K.: Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier. Digit. Signal Proc. 23, 1071–1083 (2013)

    Article  MathSciNet  Google Scholar 

  25. Chilukuri, M.V.; Dash, P.K.: Multiresolution S-transform-based fuzzy recognition system for power quality events. IEEE Trans. Power Deliv. 19, 323–330 (2004)

    Article  Google Scholar 

  26. Zhao, F.; Yang, R.: Power-quality disturbance recognition using S-transform. IEEE Trans. Power Deliv. 22, 944–950 (2007)

    Article  Google Scholar 

  27. Salem, M.E.; Mohamed, A.; Samad, S.A.: Rule based system for power quality disturbance classification incorporating S-transform features. Expert Syst. Appl. 37, 3229–3235 (2010)

    Article  Google Scholar 

  28. Hasheminejad, S.; Esmaeili, S.; Jazebi, S.: Power quality disturbance classification using S-transform and hidden Markov model. Electr. Power Compon. Syst. 40, 1160–1182 (2012)

    Article  Google Scholar 

  29. Biswal, M.; Dash, P.K.: Estimation of time-varying power quality indices with an adaptive window-based fast generalized S-transform. IET Sci. Meas. Technol. 6, 189–197 (2012)

    Article  Google Scholar 

  30. Reddy, M.J.B.; Sagar, K.; Mohanta, D.K.: A multifunctional real-time power quality monitoring system using Stockwell transform. IET Sci. Meas. Technol. 8, 155–169 (2014)

    Article  Google Scholar 

  31. Uyar, M.; Yildirim, S.; Gencoglu, M.T.: An expert system based on S-transform and neural network for automatic classification of power quality disturbances. Expert Syst. Appl. 36, 5962–5975 (2009)

    Article  Google Scholar 

  32. Bhende, C.N.; Mishra, S.; Panigrahi, B.K.: Detection and classification of power quality disturbances using S-transform and modular neural network. Electr. Power Syst. Res. 78, 122–128 (2008)

    Article  Google Scholar 

  33. Mishra, S.; Bhende, C.N.; Panigrahi, B.K.: Detection and classification of power quality disturbances using S-transform and probabilistic neural network. IEEE Trans. Power Deliv. 23, 280–287 (2008)

    Article  Google Scholar 

  34. Naik, C.A.; Kundu, P.: Power quality disturbance classification employing S-transform and three-module artificial neural network. Int. Trans. Electr. Energy Syst. 24, 1301–1322 (2014)

    Article  Google Scholar 

  35. de la Rosa, J.J.G.; Muñoz, A.M.; Gallego, A.; Piotrkowski, R.; Castro, E.: Higher-order characterization of power quality transients and their classification using competitive layers. Measurement 42, 478–484 (2009)

    Article  Google Scholar 

  36. Moravej, Z.; Pazoki, M.; Niasati, M.; Abdoos, A.A.: A hybrid intelligence approach for power quality disturbances detection and classification. Int. Trans. Electr. Energy Syst. 23, 914–929 (2013)

    Article  Google Scholar 

  37. IEEE recommended practice for monitoring electric power quality. IEEE Std. 1159–2009 (Revision of IEEE Std. 1159–1995), pp 1–81 (2009). doi:10.1109/IEEESTD.2009.5154067

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran

    Ali Enshaee

  2. Electrical Engineering Department, College of Engineering, Payame Noor University, Isfahan, Iran

    Parisa Enshaee

Authors
  1. Ali Enshaee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Parisa Enshaee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ali Enshaee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Enshaee, A., Enshaee, P. A New S-Transform-Based Method for Identification of Power Quality Disturbances. Arab J Sci Eng 43, 2817–2832 (2018). https://doi.org/10.1007/s13369-017-2895-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-017-2895-2

Keywords

Search

Navigation