Classification of Underlying Causes of Power Quality Disturbances: Deterministic versus Statistical Methods

  • Math H.J. BollenEmail author
  • Irene Y.H. Gu
  • Peter G.V. Axelberg
  • Emmanouil Styvaktakis
Open Access
Research Article
Part of the following topical collections:
  1. Emerging Signal Processing Techniques for Power Quality Applications


This paper presents the two main types of classification methods for power quality disturbances based on underlying causes: deterministic classification, giving an expert system as an example, and statistical classification, with support vector machines (a novel method) as an example. An expert system is suitable when one has limited amount of data and sufficient power system expert knowledge; however, its application requires a set of threshold values. Statistical methods are suitable when large amount of data is available for training. Two important issues to guarantee the effectiveness of a classifier, data segmentation, and feature extraction are discussed. Segmentation of a sequence of data recording is preprocessing to partition the data into segments each representing a duration containing either an event or a transition between two events. Extraction of features is applied to each segment individually. Some useful features and their effectiveness are then discussed. Some experimental results are included for demonstrating the effectiveness of both systems. Finally, conclusions are given together with the discussion of some future research directions.


Support Vector Machine Statistical Method Information Technology Support Vector Feature Extraction 


  1. 1.
    Khan AK: Monitoring power for the future. Power Engineering Journal 2001,15(2):81-85. 10.1049/pe:20010204CrossRefGoogle Scholar
  2. 2.
    McGranaghan M: Trends in power quality monitoring. IEEE Power Engineering Review 2001,21(10):3-9, 21. 10.1109/39.954579CrossRefGoogle Scholar
  3. 3.
    Bollen MHJ: Understanding Power Quality Problems: Voltage Sags and Interruptions. IEEE Press, New York, NY, USA; 1999.CrossRefGoogle Scholar
  4. 4.
    Angrisani L, Daponte P, D'Apuzzo M: Wavelet network-based detection and classification of transients. IEEE Transactions on Instrumentation and Measurement 2001,50(5):1425-1435. 10.1109/19.963220CrossRefGoogle Scholar
  5. 5.
    Bollen MHJ, Gu IYH: Signal Processing of Power Quality Disturbances. IEEE Press, New York, NY, USA; 2006.CrossRefGoogle Scholar
  6. 6.
    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. 10.1109/61.974212CrossRefGoogle Scholar
  7. 7.
    Dash PK, Mishra S, Salama MMA, Liew AC: Classification of power system disturbances using a fuzzy expert system and a Fourier Linear Combiner. IEEE Transactions on Power Delivery 2000,15(2):472-477. 10.1109/61.852971CrossRefGoogle Scholar
  8. 8.
    Gaing Z-L: Implementation of power disturbance classifier using wavelet-based neural networks. IEEE Bologna PowerTech Conference, June 2003, Bologna, Italy 3: 7.Google Scholar
  9. 9.
    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.2004.835281CrossRefGoogle Scholar
  10. 10.
    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. 10.1109/61.796242CrossRefGoogle Scholar
  11. 11.
    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. 10.1109/61.997947CrossRefGoogle Scholar
  12. 12.
    Huang S-J, Yang T-M, Huang J-T: FPGA realization of wavelet transform for detection of electric power system disturbances. IEEE Transactions on Power Delivery 2002,17(2):388-394. 10.1109/61.997905CrossRefGoogle Scholar
  13. 13.
    Kezunovic M, Liao Y: A novel software implementation concept for power quality study. IEEE Transactions on Power Delivery 2002,17(2):544-549. 10.1109/61.997935CrossRefGoogle Scholar
  14. 14.
    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:19980809MathSciNetCrossRefGoogle Scholar
  15. 15.
    Santoso S, Powers EJ, Grady WM, Parsons AC: Power quality disturbance waveform recognition using wavelet-based neural classifier—part 1: theoretical foundation. IEEE Transactions on Power Delivery 2000,15(1):222-228. 10.1109/61.847255CrossRefGoogle Scholar
  16. 16.
    Santoso S, Powers EJ, Grady WM, Parsons AC: Power quality disturbance waveform recognition using wavelet-based neural classifier—part 2: application. IEEE Transactions on Power Delivery 2000,15(1):229-235. 10.1109/61.847256CrossRefGoogle Scholar
  17. 17.
    Santoso S, Lamoree JD, Grady WM, Powers EJ, Bhatt SC: A scalable PQ event identification system. IEEE Transactions on Power Delivery 2000,15(2):738-743. 10.1109/61.853013CrossRefGoogle Scholar
  18. 18.
    Santoso S, Lamoree JD: Power quality data analysis: from raw data to knowledge using knowledge discovery approach. Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, July 2000, Seattle, Wash, USA 1: 172–177.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. 10.1109/61.997911CrossRefGoogle Scholar
  20. 20.
    Styvaktakis E: Automating power quality analysis, Ph.D. thesis. Chalmers University of Technology, Göteborg, Sweden; 2002.Google Scholar
  21. 21.
    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.2004.829940CrossRefGoogle Scholar
  22. 22.
    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.2004.829869CrossRefGoogle Scholar
  23. 23.
    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:20020014Google Scholar
  24. 24.
    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.2003.820178CrossRefGoogle Scholar
  25. 25.
    Goldberger J, Burshtein D, Franco H: Segmental modeling using a continuous mixture of nonparametric models. IEEE Transactions on Speech and Audio Processing 1999,7(3):262-271. 10.1109/89.759032CrossRefGoogle Scholar
  26. 26.
    Vapnik V: The Nature of Statistical Learning Theory. Springer, New York, NY, USA; 1995.CrossRefGoogle Scholar
  27. 27.
    Cowell RG, Lauritzen SL, Spiegelhater DJ: Probabilistic Networks and Expert Systems. 2nd edition. Springer, New York, NY, USA; 2003.Google Scholar
  28. 28.
    Shawe-Taylor J, Cristianini N: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge, UK; 2004.CrossRefGoogle Scholar
  29. 29.
    Burges CJC: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998,2(2):121-167. 10.1023/A:1009715923555CrossRefGoogle Scholar
  30. 30.
    Müller K-R, Mika S, Rätsch G, Tsuda K, Schölkopf B: An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 2001,12(2):181-201. 10.1109/72.914517CrossRefGoogle Scholar
  31. 31.
    Cristianini N, Shawe-Taylor J: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge, UK; 2000.zbMATHGoogle Scholar
  32. 32.
    Bertsekas DP: Nonlinear Programming. Athena Scientific, Belmont, Mass, USA; 1995.zbMATHGoogle Scholar
  33. 33.
    Hsu C-W, Chang C-C, Lin C-J: A practical guide to support vector classification. LIBSVM—A library for Support Vector Machines, LIBSVM—A library for Support Vector Machines,
  34. 34.
    Kosko B: Fuzzy Engineering. Prentice-Hall, Upper Saddle River, NJ, USA; 1997.zbMATHGoogle Scholar

Copyright information

© Bollen et al. 2007

Open AccessThis 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.

Authors and Affiliations

  • Math H.J. Bollen
    • 1
    • 2
    Email author
  • Irene Y.H. Gu
    • 3
  • Peter G.V. Axelberg
    • 3
  • Emmanouil Styvaktakis
    • 4
  1. 1.STRI ABLudvikaSweden
  2. 2.EMC-on-Site, Luleå University of TechnologySkellefteåSweden
  3. 3.Department of Signals and SystemsChalmers University of TechnologyGothenburgSweden
  4. 4.The Hellenic Transmission System OperatorAthensGreece

Personalised recommendations