Characterization of Power Quality Disturbances and Their Efficient Classification

  • Laxmipriya Samal
  • Hemanta Kumar PaloEmail author
  • Badri Narayan Sahu
  • Debashisa Samal
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 665)


Characterization and classification of power quality (PQ) disturbances are an essential component in the field of power engineering to meet consumer demands. Accurate analysis of supply power, its processing, and distribution requires identification of the noise and disturbances associated during power generation, transmission, and distribution. In general, pure-tone power signals are non-stationary with both time- and frequency-varying statistical parameters. Thus, the use of either the time domain or frequency domain analysis cannot characterize or classify the PQ signal adequately. This motivates the authors to approach the problem domain employing time–frequency (TF) characterization using a spectrogram initially. TF distribution is one of the best application tools for PQ analysis and is emphasized using the short-time Fourier transform (STFT) and wavelet-based features in this paper. Finally, the TF-based features extracted from the normal and different PQ disturbances are applied to an efficient probabilistic neural network (PNN) model for classification. We have shown that PNN with TF-based wavelet features provides an efficient classification result as compared to other chosen techniques.


Characterization Power quality Disturbances Accuracy Probabilistic neural network Classification 


  1. 1.
    Abdelsalam, A.A., Eldesouky, A.A., Sallam, A.A.: Characterization of power quality disturbances using hybrid technique of linear Kalman filter and fuzzy-expert system. Electr. Power Syst. Res. 83(1), 41–50 (2012)CrossRefGoogle Scholar
  2. 2.
    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 Process. 23(4), 1071–1083 (2013)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Dehghani, H., Vahidi, B., Naghizadeh, R.A., Hosseinian, S.H.: Power quality disturbance classification using a statistical and wavelet-based hidden Markov model with Dempster-Shafer algorithm. Int. J. Electr. Power Energy Syst. 47, 368–377 (2013)CrossRefGoogle Scholar
  4. 4.
    Erişti, H., Yıldırım, Ö., Erişti, B., Demir, Y.: Automatic recognition system of underlying causes of power quality disturbances based on S-transform and Extreme Learning Machine. Int. J. Electr. Power Energy Syst. 61, 553–562 (2014)CrossRefGoogle Scholar
  5. 5.
    Huang, N., Xu, D., Liu, X., Lin, L.: Power quality disturbances classification based on S-transform and probabilistic neural network. Neurocomputing 98, 12–23 (2012)CrossRefGoogle Scholar
  6. 6.
    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(12), 1552–1561 (2010)CrossRefGoogle Scholar
  7. 7.
    De Yong, D., Bhowmik, S., Magnago, F.: An effective power quality classifier using wavelet transform and support vector machines. Expert Syst. Appl. 42(15–16), 6075–6081 (2015)CrossRefGoogle Scholar
  8. 8.
    Shen, Y., Abubakar, M., Liu, H., Hussain, F.: Power quality disturbance monitoring and classification based on improved PCA and convolution neural network for wind-grid distribution systems. Energies 12(7), 1280 (2019)CrossRefGoogle Scholar
  9. 9.
    Ribeiro, M.V., Szczupak, J., Iravani, M.R., Gu, I.Y., Dash, P.K., Mamishev, A.V.: Emerging Signal Processing Techniques for Power Quality Applications (2007)Google Scholar
  10. 10.
    Palo, H.K., Mohanty, M.N.: Comparative analysis of neural networks for speech emotion recognition. Int. J. Eng. Technol. 7, 112–116 (2018)Google Scholar
  11. 11.
    Palo, H.K., Mohanty, M.N., Chandra, M.: Efficient feature combination techniques for emotional speech classification. Int. J. Speech Technol. 19, 135–150 (2016)CrossRefGoogle Scholar
  12. 12.
    Palo, H.K., Sagar, S.: Comparison of neural network models for speech emotion recognition. In: 2018 2nd International Conference of Data Science and Business Analytics, pp. 127–131. IEEE (2018)Google Scholar
  13. 13.
    Palo, H.K., Mohanty, M.N.: Wavelet-based feature combination for recognition of emotions. Ain Shams Eng. J. 9, 1799–1806 (2018)CrossRefGoogle Scholar
  14. 14.
    Palo, H.K., Chandra, M., Mohanty, M.N.: Recognition of human speech emotion using variants of Mel-frequency cepstral coefficients. Advances in Systems, Control and Automation, pp. 491–498. Springer, Singapore (2018)CrossRefGoogle Scholar
  15. 15.
    Abdullah, A.R., Sha’ameri, A.Z., Sidek, A.R.M., Shaari, M.R.: Detection and classification of power quality disturbances using time-frequency analysis technique. In: 2007 5th Student Conference on Research and Development, pp. 1–6. IEEE (2007)Google Scholar
  16. 16.
    Palo, H.K., Sagar, S.: Characterization and classification of speech emotion with spectrograms. In: 2018 IEEE 8th International Advance Computing Conference (IACC), pp. 309–313. IEEE (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Laxmipriya Samal
    • 1
  • Hemanta Kumar Palo
    • 1
    Email author
  • Badri Narayan Sahu
    • 1
  • Debashisa Samal
    • 1
  1. 1.Institute of Technical Education and Research, Siksha ‘O’ Anusandhan (Deemed to be University)BhubaneswarIndia

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