Classification of Power Quality Disturbances Using GA Based Optimal Feature Selection

  • K. R. Krishnanand
  • Santanu Kumar Nayak
  • B. K. Panigrahi
  • V. Ravikumar Pandi
  • Priyadarshini Dash
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

This paper presents a novel technique for power quality disturbance classification. Wavelet Transform (WT) has been used to extract some useful features of the power system disturbance signal and Gray-coded Genetic Algorithm (GGA) have been used for feature dimension reduction in order to achieve high classification accuracy. Next, a Probabilistic Neural Network (PNN) has been trained using the optimal feature set selected by GGA for automatic Power Quality (PQ) disturbance classification. Considering ten types of PQ disturbances, simulations have been carried out which show that the combination of feature extraction by WT followed by feature reduction using GGA increases the testing accuracy of PNN while classifying PQ signals.

Keywords

Gray-coded Genetic Algorithm Power quality disturbances Wavelet transform Probabilistic Neural Network 

References

  1. 1.
    Bollen, M.H.J.: Understanding Power Quality: Voltage sags and Interruptions. IEEE Press, NewYork (2000)Google Scholar
  2. 2.
    Daubechies, I.: The wavelet transform, time/frequency location and signal analysis. IEEE Transactions on Information Theory 36, 961–1005 (1990)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Mallat, S.G.: A theory of multi resolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)MATHCrossRefGoogle Scholar
  4. 4.
    Meyer, Y.: Wavelets and Operators. Cambridge University Press, London (1992)MATHGoogle Scholar
  5. 5.
    Santoso, S., Powers, E.J., Grady, W.M., Hofmann, P.: Power quality assessment via wavelet transform analysis. IEEE Transactions on Power Delivery 11, 924–930 (1996)CrossRefGoogle Scholar
  6. 6.
    Gaouda, A.M., Salama, M.M.A., Sultan, M.K., Chikhani, A.Y.: Power Quality Detection and Classification Using Wavelet-Multi resolution Signal Decomposition. IEEE Transactions on Power Delivery 14, 1469–1476 (1999)CrossRefGoogle Scholar
  7. 7.
    Santoso, S., Powers, E.J., Grady, W.M., Parsons, A.: Power quality disturbance waveform recognition using wavelet-based neural classifier, Part 1: theoretical foundation. In: The 1997 IEEE/PES Winter Meeting, New York,U.S.A (1997)Google Scholar
  8. 8.
    Gaing, Z.L.: Wavelet-Based Neural Network for Power Disturbance Recognition and Classification. IEEE Trans. on Power Delivery 19, 1560–1568 (2004)CrossRefGoogle Scholar
  9. 9.
    Specht, D.F.: Probabilistic neural networks. Neural Networks 3, 109–118 (1990)CrossRefGoogle Scholar
  10. 10.
    Panigrahi, B.K., Ravikumar Pandi, V.: Optimal feature selection for classification of power quality disturbances using wavelet packet-based fuzzy k-nearest neighbour algorithm. IET Generation Trans. Distr. 3, 296–306 (2009)CrossRefGoogle Scholar
  11. 11.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1999)Google Scholar
  12. 12.
    Chakraborti, N., Mishra, P., Erkoc, S.: A Study of the Cu Clusters Using Gray-Coded Genetic Algorithms and Differential Evolution. Journal of Phase Equilibria and diffusion 25, 16–21 (2004)CrossRefGoogle Scholar
  13. 13.
    Yang, X., Yang, Z., Yin, X., Li, J.: Chaos gray-coded genetic algorithm and its application for pollution source identifications in convection–diffusion equation. Comm. In non linear science and numerical simulation 13, 1676–1688 (2008)CrossRefGoogle Scholar
  14. 14.
    MATLAB, Math Works, Inc., Natick, MA, USA (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • K. R. Krishnanand
    • 1
  • Santanu Kumar Nayak
    • 1
  • B. K. Panigrahi
    • 2
  • V. Ravikumar Pandi
    • 2
  • Priyadarshini Dash
    • 3
  1. 1.Department of Electrical EngineeringSilicon Institute of TechnologyBhubaneswarIndia
  2. 2.Department of Electrical EngineeringIndian Institute of TechnologyDelhiIndia
  3. 3.Department of Electrical EngineeringNIT DurgapurIndia

Personalised recommendations