A Nonlinear Analysis of Partial Discharge Signal

부분방전 신호의 비 선형적 해석

  • Published : 2000.03.01

Abstract

The partial discharge(PD) signal, may seems to be stochastic and merely random, was investigated using the method to discern between chaos and random signal, e.g. correlation integral, Lyapunov characteristic exponents and etc. For the purpose of obtaining experimental data, partial discharge detecting system via computer aided acoustic sensor, detect PD signal from the insulating system, was used. While this method is very different from typical statistical analysis from the point of view of a nonlinear analysis, it can provide better interpretable criterion according to the time evolution with a degradation process in the same type insulating system.

Keywords

References

  1. R. E. James and B. T. Phung, 'Development of computer-based measurements and their application to PD pattern analysis', IEEE Trans. Die. and Elect. Insul. vol. 2, 838-856, 1995 https://doi.org/10.1109/94.469978
  2. Sung-Hong Kim et al, 'A life prediction of insulation degradation using computer system', ICEE'98, Proceedings, vol. 2, 869-872, 1998
  3. N. H. Packard, J. D. Farmer and R. S.Shaw, ' Geometry from a time series', Physical Review Letters, vol. 45, 712-715, 1980 https://doi.org/10.1103/PhysRevLett.45.712
  4. F. Takens, 'Detecting strange attractors in turbulence', Lecture Notes in mathematics, Springer-Verlag New York Inc., 366-381. 1981
  5. D. S. Broomhead and Gregory P. king, 'Extracting qualitative dynamics from experimental data', Physica D, vol. 20, 217-236, 1986 https://doi.org/10.1016/0167-2789(86)90031-X
  6. Mathew B. Kennel, Reggie Brown and Herry D. I. Abarbanel, 'Determining embedding dimension for phase-space reconstruction using a geometrical construction', Physical Review A, vol. 45, 3403-3411, 1992 https://doi.org/10.1103/PhysRevA.45.3403
  7. J. P. Eckmann and D. Ruelle, 'Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems', Physica D. vol. 56, 185-187, 1992 https://doi.org/10.1016/0167-2789(92)90023-G
  8. Kathleen T. Alligoog, Tim D. Sauer and James A. Yorke, ' CHAOS - An Introduction to dynamical systems', Springer-Verlag New York Inc., pp 387-420, 1996
  9. James Theiler, 'Statistical precision of dimension estimators', Physical Review A. vol. 41, 3038, 1990 https://doi.org/10.1103/PhysRevA.41.3038
  10. J. D. Fraser and H.L. Swinney, 'Independent coordinates for strange attractors from mutual information', Physical Review A. vol. 33, 1134, 1986
  11. A. M. Albano, J. Muench, C. Schwarts, A. I. Mess and P. E. Rapp, 'Singular-value decomposition and the Grassberger-Procaccia algorithm', Physical Review A. vol. 38, 3017, 1988 https://doi.org/10.1103/PhysRevA.38.3017
  12. Zuo-bing Wu, 'Remark on metric analysis of reconstructed dynamics from time series', Physica D. vol. 85, 485-495, 1995 https://doi.org/10.1016/0167-2789(95)00180-C
  13. M. Sano and Y. Swada, 'Measurement of the Lyapunov spectrum from a chaotic time series', Physical Review Letters, vol. 55, 10, 1082-1085, 1985 https://doi.org/10.1103/PhysRevLett.55.1082
  14. J. P. Eckmann, S. O. Kamnphorst, D. Ruelle and S. Ciliberto, 'Lyapunov exponents from time series', Physical Review A, vol. 34, 4971-4979, 1986 https://doi.org/10.1103/PhysRevA.34.4971
  15. Edited by Kazuyuki Aihara, 'Brain and Chaos', Ohm Inc., pp. 125-136, 1994
  16. Lando Caiani, Lapo Casetti, Cecilia Clementi and Marco Pettini, 'Geometry of dynamics, Lyapunov exponents and phase transitions', Physical Review Letters, vol. 79, No. 22, 1997 https://doi.org/10.1103/PhysRevLett.79.4361
  17. Y. C. Lai and David Learner, 'Effective scaling region for computing the correlation dimension from chaotic time series', Physica D, vol. 115, 1, 1998