Skip to main content

Classification of Single and Multiple Disturbances in Electric Signals


This paper discusses and presents a different perspective for classifying single and multiple disturbances in electric signals, such as voltage and current ones. Basically, the principle of divide to conquer is applied to decompose the electric signals into what we call primitive signals or components from which primitive patterns can be independently recognized. A technique based on such concept is introduced to demonstrate the effectiveness of such idea. This technique decomposes the electric signals into three main primitive components. In each primitive component, few high-order-statistics- (HOS-) based features are extracted. Then, Bayes' theory-based techniques are applied to verify the ocurrence or not of single or multiple disturbances in the electric signals. The performance analysis carried out on a large number of data indicates that the proposed technique outperforms the performance attained by the technique introduced by He and Starzyk. Additionally, the numerical results verify that the proposed technique is capable of offering interesting results when it is applied to classify several sets of disturbances if one cycle of the main frequency is considered, at least.


  1. 1.

    Ribeiro MV: Signal processing techniques for power line communication and power quality applications, Ph.D. thesis. Department of Communications, University of Campinas, Sao Paulo, Brazil; 2005.

    Google Scholar 

  2. 2.

    He H, Starzyk JA: A self-organizing learning array system for power quality classification based on wavelet transform. IEEE Transactions on Power Delivery 2006,21(1):286-295. 10.1109/TPWRD.2005.852392

    Article  Google Scholar 

  3. 3.

    Abdel-Galil TK, Kamel M, Youssef AM, El-Saadany EF, Salama MMA: Power quality disturbance classification using the inductive inference approach. IEEE Transactions on Power Delivery 2004,19(4):1812-1818. 10.1109/TPWRD.2003.822533

    Article  Google Scholar 

  4. 4.

    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 

  5. 5.

    Wang M, Mamishev AV: Classification of power quality events using optimal time-frequency representations—part 1: theory. IEEE Transactions on Power Delivery 2004,19(3):1488-1495. 10.1109/TPWRD.2004.829940

    Article  Google Scholar 

  6. 6.

    Azam MS, Tu F, Pattipati KR, Karanam R: A dependency model-based approach for identifying and evaluating power quality problems. IEEE Transactions on Power Delivery 2004,19(3):1154-1166. 10.1109/TPWRD.2003.822537

    Article  Google Scholar 

  7. 7.

    Wang M, Rowe GI, Mamishev AV: Classification of power quality events using optimal time-frequency representations—part 2: application. IEEE Transactions on Power Delivery 2004,19(3):1496-1503. 10.1109/TPWRD.2004.829869

    Article  Google Scholar 

  8. 8.

    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 

  9. 9.

    Chung J, Powers EJ, Grady WM, Bhatt SC: Power disturbance classifier using a rule-based method and wavelet packet-based hidden Markov model. IEEE Transactions on Power Delivery 2002,17(1):233-241. 10.1109/61.974212

    Article  Google Scholar 

  10. 10.

    Ibrahim WRA, Morcos MM: A power quality perspective to system operational diagnosis using fuzzy logic and adaptive techniques. IEEE Transactions on Power Delivery 2003,18(3):903-909. 10.1109/TPWRD.2003.813885

    Article  Google Scholar 

  11. 11.

    Lee IWC, Dash PK: S-transform-based intelligent system for classification of power quality disturbance signals. IEEE Transactions on Industrial Electronics 2003,50(4):800-805. 10.1109/TIE.2003.814991

    Article  Google Scholar 

  12. 12.

    Dash PK, Panigrahi BK, Sahoo DK, Panda G: Power quality disturbance data compression, detection, and classification using integrated spline wavelet and S-transform. IEEE Transactions on Power Delivery 2003,18(2):595-600. 10.1109/TPWRD.2002.803824

    Article  Google Scholar 

  13. 13.

    Huang J, Negnevitsky M, Nguyen DT: A neural-fuzzy classifier for recognition of power quality disturbances. IEEE Transactions on Power Delivery 2002,17(2):609-616. 10.1109/61.997947

    Article  Google Scholar 

  14. 14.

    Gaouda AM, Kanoun SH, Salama MMA, Chikhani AY: Wavelet-based signal processing for disturbance classification and measurement. IEE Proceedings: Generation, Transmission and Distribution 2002,149(3):310-318. 10.1049/ip-gtd:20020119

    Google Scholar 

  15. 15.

    Kezunovic M, Liao Y: A novel software implementation concept for power quality study. IEEE Transactions on Power Delivery 2002,17(2):544-549. 10.1109/61.997935

    Article  Google Scholar 

  16. 16.

    Lee JY, Won YJ, Jeong J-M, Nam SW: Classification of power disturbance using feature extraction in time-frequency plane. Electronics Letters 2002,38(15):833-835. 10.1049/el:20020562

    Article  Google Scholar 

  17. 17.

    Hoang TA, Nguyen DT: Improving training of radial basis function network for classification of power quality disturbances. Electronics Letters 2002,38(17):976-977. 10.1049/el:20020658

    Article  Google Scholar 

  18. 18.

    Wijayakulasooriya JV, Putrus GA, Minns PD: Electric power quality disturbance classification using self-adapting artificial neural networks. IEE Proceedings: Generation, Transmission and Distribution 2002,149(1):98-101. 10.1049/ip-gtd:20020014

    Google Scholar 

  19. 19.

    Styvaktakis E, Bollen MHJ, Gu IYH: Expert system for classification and analysis of power system events. IEEE Transactions on Power Delivery 2002,17(2):423-428. 10.1109/61.997911

    Article  Google Scholar 

  20. 20.

    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 

  21. 21.

    Kezunovic M, Liao Y: A new method for classification and characterization of voltage sags. Electric Power Systems Research 2001,58(1):27-35. 10.1016/S0378-7796(01)00104-3

    Article  Google Scholar 

  22. 22.

    Dash PK, Jena RK, Salama MMA: Power quality monitoring using an integrated Fourier linear combiner and fuzzy expert system. International Journal of Electrical Power & Energy System 1999,21(7):497-506. 10.1016/S0142-0615(99)00013-7

    Article  Google Scholar 

  23. 23.

    Gaouda AM, Salama MMA, Sultan MR, Chikhani AY: Power quality detection and classification using wavelet-multiresolution signal decomposition. IEEE Transactions on Power Delivery 1999,14(4):1469-1476. 10.1109/61.796242

    Article  Google Scholar 

  24. 24.

    Huang S-J, Hsieh C-T, Huang C-L: Application of wavelets to classify power system disturbances. Electric Power Systems Research 1998,47(2):87-93. 10.1016/S0378-7796(98)00053-4

    Article  Google Scholar 

  25. 25.

    Lee CH, Nam SW: Efficient feature vector extraction for automatic classification of power quality disturbances. Electronics Letters 1998,34(11):1059-1061. 10.1049/el:19980809

    MathSciNet  Article  Google Scholar 

  26. 26.

    Lee JS, Lee CH, Kim JO, Nam SW: Classification of power quality disturbances using orthogonal polynomial approximation and bispectra. Electronics Letters 1997,33(18):1522-1524. 10.1049/el:19971028

    Article  Google Scholar 

  27. 27.

    Ghosh AK, Lubkeman DL: The classification of power system disturbance waveforms using a neural network approach. IEEE Transactions on Power Delivery 1995,10(1):109-115. 10.1109/61.368408

    Article  Google Scholar 

  28. 28.

    Youssef AM, Abdel-Galil TK, El-Saadany EF, Salama MMA: Disturbance classification utilizing dynamic time warping classifier. IEEE Transactions on Power Delivery 2004,19(1):272-278. 10.1109/TPWRD.2003.820178

    Article  Google Scholar 

  29. 29.

    Duque CA, Ribeiro MV, Ramos FR, Szczupak J: Power quality event detection based on the divide and conquer principle and innovation concept. IEEE Transactions on Power Delivery 2005,20(4):2361-2369. 10.1109/TPWRD.2005.855478

    Article  Google Scholar 

  30. 30.

    Ribeiro MV, Pereira JLR: An efficient method for the classification of isolated and multiple disturbances in power line signals. Proceedings of the 12th International Conference on Harmonics and Quality of Power (ICHQP '06), October 2006, Cascais, Portugal

    Google Scholar 

  31. 31.

    Ferreira DD, Cerqueira AS, Ribeiro MV, Duque CA: HOS-based method for power quality event classification. Proceedings of the 14th European Signal Processing Conference (EUSIPCO '06), September 2006, Florence, Italy 1: 200–206.

    Google Scholar 

  32. 32.

    Cerqueira AS, Ribeiro MV, Duque CA, Ferreira DD: Power quality events recognition using a SVM-based method. to appear in Electric Power System Research

  33. 33.

    van Trees HL: Detection, Estimation and Modulation Theory, Part I. Springer, New York, NY, USA; 1968.

    MATH  Google Scholar 

  34. 34.

    van Trees HL: Detection, Estimation and Modulation Theory, Part III. Springer, New York, NY, USA; 1971.

    MATH  Google Scholar 

  35. 35.

    McDonough RN, Whalen AD: Detection of Signals in Noise. Academic Press, London, UK; 1995.

    Google Scholar 

  36. 36.

    IEEE 519 Working Group : IEEE 519–1995 recommended practices and requirements for harmonic control in electrical power systems institute of electrical and electronics engineers. 1992.

    Google Scholar 

  37. 37.

    Carvalho JR, Gomes PH, Cerqueira AS, Ribeiro MV, Duque CA, Szczupak J: PLL based multirate harmonic estimation. IEEE PES General Meeting, 2007, Tampa, Fla, USA

    Google Scholar 

  38. 38.

    Ribeiro MV, Marques CAG, Duque CA, Cerqueira AS, Pereira JLR: Detection of disturbances in voltage signals for power quality analysis using HOS. EURASIP Journal on Advances in Signal Processing 2007, 2007: 13 pages.

    MATH  Google Scholar 

  39. 39.

    Kauraniemi J, Laakso TI, Hartimo I, Ovaska SJ: Delta operator realizations of direct-form IIR filters. IEEE Transactions on Circuits and Systems II 1998,45(1):41-52. 10.1109/82.659455

    Article  Google Scholar 

  40. 40.

    Middleton RH, Goodwin GC: Improved finite word length characteristics in digital control using delta operators. IEEE Transactions on Automatic Control 1986,31(11):1015-1021. 10.1109/TAC.1986.1104162

    Article  Google Scholar 

  41. 41.

    Jain AK, Duin RPW, Mao J: Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000,22(1):4-37. 10.1109/34.824819

    Article  Google Scholar 

  42. 42.

    Mendel JM: Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proceedings of the IEEE 1991,79(3):278-305. 10.1109/5.75086

    Article  Google Scholar 

  43. 43.

    Nikias C, Mendel J: Signal processing with higher-order statistics. IEEE Transactions on Signal Processing 1999,41(1):10-38.

    Google Scholar 

  44. 44.

    Nikias CL, Petropulu AP: Hiher-Order Spectra Analysis—A Nonlinear Signal Processing Framework. Prentice-Hall, Englewood Cliffs, NJ, USA; 1993.

    MATH  Google Scholar 

  45. 45.

    Frisch M, Messer H: Transient signal detection using prior information in the likelihood-ratio-test. IEEE Transactions on Signal Processing 1993,41(6):2177-2192. 10.1109/78.218145

    Article  Google Scholar 

  46. 46.

    Gerek ÖN, Ece DG: Power-quality event analysis using higher order cumulants and quadratic classifiers. IEEE Transactions on Power Delivery 2006,21(2):883-889. 10.1109/TPWRD.2006.870989

    Article  Google Scholar 

  47. 47.

    Picinbono B: Polyspectra of ordered signals. IEEE Transactions on Information Theory 1999,45(7):2239-2252. 10.1109/18.796366

    MathSciNet  Article  Google Scholar 

  48. 48.

    Theodoridis S, Koutroumbas K: Pattern Recognition. Academic Press, San Diego, Calif, USA; 1999.

    MATH  Google Scholar 

  49. 49.

    IEEE 1159 Working Group : IEEE 1159–1995 recommended practice on monitoring electrical power quality. 1995.

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Moisés Vidal Ribeiro.

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

Ribeiro, M.V., Pereira, J.L.R. Classification of Single and Multiple Disturbances in Electric Signals. EURASIP J. Adv. Signal Process. 2007, 056918 (2007).

Download citation


  • Information Technology
  • Performance Analysis
  • Quantum Information
  • Electric Signal
  • Main Frequency