Skip to main content

Wavelet Transform for Processing Power Quality Disturbances


The emergence of power quality as a topical issue in power systems in the 1990s largely coincides with the huge advancements achieved in the computing technology and information theory. This unsurprisingly has spurred the development of more sophisticated instruments for measuring power quality disturbances and the use of new methods in processing and analyzing the measurements. Fourier theory was the core of many traditional techniques and it is still widely used today. However, it is increasingly being replaced by newer approaches notably wavelet transform and especially in the post-event processing of the time-varying phenomena. This paper reviews the use of wavelet transform approach in processing power quality data. The strengths, limitations, and challenges in employing the methods are discussed with consideration of the needs and expectations when analyzing power quality disturbances. Several examples are given and discussions are made on the various design issues and considerations, which would be useful to those contemplating adopting wavelet transform in power quality applications. A new approach of combining wavelet transform and rank correlation is introduced as an alternative method for identifying capacitor-switching transients.


  1. 1.

    IEC 61000-2-1 : Electromagnetic compatibility (EMC)—part 2: environment - section 1: description of the environment - electromagnetic environment for low-frequency conducted disturbances and signalling in public power supply systems. 1st edition, 1990

    Google Scholar 

  2. 2.

    IEEE Std. 1159–1995 : IEEE Recommended Practice for Monitoring Electric Power Quality. 1995.

    Google Scholar 

  3. 3.

    IEC 61000-2-2 : Electromagnetic compatibility (EMC)—part 2-2: environment - compatibility levels for low-frequency conducted disturbances and signalling in public low-voltage power supply systems. 2nd edition, 2002

    Google Scholar 

  4. 4.

    Gu IY-H, Styvaktakis E: Bridge the gap: signal processing for power quality applications. Electric Power Systems Research 2003,66(1):83-96. 10.1016/S0378-7796(03)00074-9

    Article  Google Scholar 

  5. 5.

    Gu IY-H, Bollen MHJ: Time-frequency and time-scale domain analysis of voltage disturbances. IEEE Transactions on Power Delivery 2000,15(4):1279-1284. 10.1109/61.891515

    Article  Google Scholar 

  6. 6.

    Bruce A, Donoho D, Gao H-Y: Wavelet analysis [for signal processing]. IEEE Spectrum 1996,33(10):26-35. 10.1109/6.540087

    Article  Google Scholar 

  7. 7.

    Graps A: An introduction to wavelets. IEEE Computational Science & Engineering 1995,2(2):50-61. 10.1109/99.388960

    Article  Google Scholar 

  8. 8.

    Fernández RMC, Rojas HND: An overview of wavelet transforms in power system. Proceedings of the 14th Power System Computational Conference (PSCC '02), June 2002, Sevilla, Spain

    Google Scholar 

  9. 9.

    Mallat S: A Wavelet Tour of Signal Processing. 2nd edition. Academic Press, San Diego, Calif, USA; 1999.

    Google Scholar 

  10. 10.

    Robertson DC, Camps OI, Mayer JS, Gish Sr WB: Wavelets and electromagnetic power system transients. IEEE Transactions on Power Delivery 1996,11(2):1050-1056. 10.1109/61.489367

    Article  Google Scholar 

  11. 11.

    Karimi M, Mokhtari H, Iravani MR: Wavelet based on-line disturbance detection for power quality applications. IEEE Transactions on Power Delivery 2000,15(4):1212-1220. 10.1109/61.891505

    Article  Google Scholar 

  12. 12.

    Wilkinson WA, Cox MD: Discrete wavelet analysis of power system transients. IEEE Transactions on Power Systems 1996,11(4):2038-2044. 10.1109/59.544682

    Article  Google Scholar 

  13. 13.

    Butler-Purry KL, Bagriyanik M: Characterization of transients in transformers using discrete wavelet transforms. IEEE Transactions on Power Systems 2003,18(2):648-656. 10.1109/TPWRS.2003.810979

    Article  Google Scholar 

  14. 14.

    Poisson O, Rioual P, Meunier M: Detection and measurement of power quality disturbances using wavelet transform. IEEE Transactions on Power Delivery 2000,15(3):1039-1044. 10.1109/61.871372

    Article  Google Scholar 

  15. 15.

    Santoso S, Grady WM, Powers EJ, Lamoree J, Bhatt SC: Characterization of distribution power quality events with Fourier and wavelet transforms. IEEE Transactions on Power Delivery 2000,15(1):247-254. 10.1109/61.847259

    Article  Google Scholar 

  16. 16.

    Hong Y-Y, Wang C-W: Switching detection/classification using discrete wavelet transform and self-organizing mapping network. IEEE Transactions on Power Delivery 2005,20(2, part 2):1662-1668. 10.1109/TPWRD.2004.833921

    Article  Google Scholar 

  17. 17.

    Zhu TX, Tso SK, Lo KL: Wavelet-based fuzzy reasoning approach to power-quality disturbance recognition. IEEE Transactions on Power Delivery 2004,19(4):1928-1935. 10.1109/TPWRD.2004.832382

    Article  Google Scholar 

  18. 18.

    Gaing Z-L: Wavelet-based neural network for power disturbance recognition and classification. IEEE Transactions on Power Delivery 2004,19(4):1560-1568. 10.1109/TPWRD.2004.835281

    Article  Google Scholar 

  19. 19.

    Lin C-H, Tsao M-C: Power quality detection with classification enhancible wavelet-probabilistic network in a power system. IEE Proceedings: Generation, Transmission and Distribution 2005,152(6):969-976. 10.1049/ip-gtd:20045177

    Article  Google Scholar 

  20. 20.

    IEC 61000-4-7 : Electromagnetic compatibility (EMC)—part 4-7: testing and measurement techniques - general guide on harmonics and interharmonics measurement and instrumentation, for power supply systems and equipment connected thereto. International Electrotechnical Commission, 2002

    Google Scholar 

  21. 21.

    Barros J, Diego RI: Application of the wavelet-packet transform to the estimation of harmonic groups in current and voltage waveforms. IEEE Transactions on Power Delivery 2006,21(1):533-535. 10.1109/TPWRD.2005.848437

    Article  Google Scholar 

  22. 22.

    Saied MM: Capacitor switching transients: analysis and proposed technique for identifying capacitor size and location. IEEE Transactions on Power Delivery 2004,19(2):759-765. 10.1109/TPWRD.2003.822953

    Article  Google Scholar 

  23. 23.

    William HP, Brian PF, Saul AT, William TV: Numerical Recipes in C: The Art of Scientific Computing. 2nd edition. Cambridge University Press, Cambridge, UK; 1992.

    Google Scholar 

  24. 24.

    Durbak DW: Modeling guidelines for switching transients, modeling and analysis of system transients. IEEE PES special publication, 1998

    Google Scholar 

  25. 25.

    Hwang W-L, Mallat S: Singularities and noise discrimination with wavelets. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '92), March 1992, San Francisco, Calif, USA 4: 377–380.

    Google Scholar 

  26. 26.

    Mallat S, Hwang W-L: Singularity detection and processing with wavelets. IEEE Transactions on Information Theory 1992,38(2, part 2):617-643. 10.1109/18.119727

    MathSciNet  Article  Google Scholar 

  27. 27.

    Daubechies I: Ten Lectures on Wavelets. SIAM, Philadelphia, Pa, USA; 1992.

    Google Scholar 

  28. 28.

    Huang S-J, Hsieh C-T, Huang C-L: Application of Morlet wavelets to supervise power system disturbances. IEEE Transactions on Power Delivery 1999,14(1):235-243. 10.1109/61.736728

    Article  Google Scholar 

  29. 29.

    User's Guide of Wavelet Toolbox MathWorks, 2004,

Download references

Author information



Corresponding author

Correspondence to S. Chen.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Chen, S., Zhu, H.Y. Wavelet Transform for Processing Power Quality Disturbances. EURASIP J. Adv. Signal Process. 2007, 047695 (2007).

Download citation


  • Information Technology
  • Power System
  • Quality Data
  • Quantum Information
  • Wavelet Transform