The Recognition Study of Impulse and Oscillation Transient Based on Spectral Kurtosis and Neural Network

  • Qiaoge Zhang
  • Zhigang Liu
  • Gang Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)


To improve the precision of classification and recognition of transient power quality disturbances, a new algorithm based on spectral kurtosis (SK) and neural network is proposed. In the proposed algorithm, Morlet complex wavelet is used to obtain the WT-based SK of two kinds of disturbances, such as the impulse transient and oscillation transient. Two characteristic quantities, i.e., the maximum value of SK and the frequencies of the signals, are chosen as the input of neural network for the classification and recognition of transient power quality disturbances. Simulation results show that the transient disturbance characteristics can be effectively extracted by WT-based SK. With RBF neural network, the two kinds of transient disturbances can be effectively classified and recognized with the method in the paper.


Impulse and Oscillation Transient Classification and Recognition Spectral Kurtosis Neural Network 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhao, J., He, Z., Jia, Y.: Classification of transient power quality disturbances based on high-order cumulants. Power System Technology 35(5), 103–110 (2011)Google Scholar
  2. 2.
    Wang, J., Xia, L., Wu, G., et al.: Analysis of power system transient signal using genetic algorithm and network. High Voltage Engineering 37(1), 170–176 (2011)Google Scholar
  3. 3.
    Lin, S., He, Z., Luo, G.: A wavelet energy moment based classification and recognition method of transient signals in power transmission lines. Power System Technology 32(20), 30–34 (2008)Google Scholar
  4. 4.
    Antoni, J.: The spectral kurtosis: a useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing 20, 282–307 (2006)CrossRefGoogle Scholar
  5. 5.
    Wang, X., He, Z., Zi, Y.: Spectral kurtosis of multiwavelet for fault diagnosis of rolling bearing. Journal of Xi An Jiaotong University 44(3), 77–81 (2010)Google Scholar
  6. 6.
    Shi, L., Zhang, Y., Mi, W.: Application of Wigner-Ville-distribution-based spectral kurtosis algorithm to fault diagnosis of rolling bearing. Journal of Vibration, Measurement & Diagnosis 31(1), 27–33 (2011)Google Scholar
  7. 7.
    Wang, Y., Liang, M.: An adaptive SK technique and its application for fault detection of rolling element bearings. Mechanical System and Signal Processing 25, 1750–1764 (2011)CrossRefGoogle Scholar
  8. 8.
    Ding, S., Xu, L., Su, C.: An optimizing method of RBF neural network based. Neural Comput & Applic. (2011)Google Scholar
  9. 9.
    Sun, X., Zheng, J., Pang, Y., Ye, C., Zhang, L.: The Application of Neural Network Model Based on Genetic Algorithm for Comprehensive Evaluation. In: Wu, Y. (ed.) ICHCC 2011. CCIS, vol. 163, pp. 229–236. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Dwyer, R.: Detection of non-Gaussian signals by frequency domain kurtosis estimation. In: Proceedings of IEEE ICASSP, vol. 8, pp. 607–610 (1983)Google Scholar
  11. 11.
    Antoni, J., Randall, R.B.: The spectral kurtosis- application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing 20, 308–331 (2006)CrossRefGoogle Scholar
  12. 12.
    Shi, L.: Rolling bearing fault detection using improved envelope analysis. Bearing (2), 36–39 (2006)Google Scholar
  13. 13.
    Omer, N.G., Dogan, G.E.: Power-quality event analysis using higher order cumulants and quadratic classifiers. IEEE Transactions on Power Delivery 21(2), 883–889 (2006)CrossRefGoogle Scholar
  14. 14.
    Juan, J.G., Antonio, M.M., Luque, A., et al.: Characterization and classification of electrical transients using higher-order statistics and neural networks. In: CIMSA 2007-IEEE (2007)Google Scholar
  15. 15.
    Juan, J.G., Antonio, M.M., Antolino, G., et al.: Higher-order characterization of power quality transients and their classification using competitive layers. Measurement 42(3), 478–484 (2009)CrossRefGoogle Scholar
  16. 16.
    Zhang, Q., Liu, H.: Application of LS-SVM in classification of power quality disturbances. Proceedings of the CSEE 28(1), 106–110 (2008)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Qiaoge Zhang
    • 1
  • Zhigang Liu
    • 1
  • Gang Chen
    • 1
  1. 1.School of Electrical EngineeringSouthwest Jiaotong UniversityChengduChina

Personalised recommendations