Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of Partial Discharge Signals Using Statistical and Pattern Recognition Technique

통계처리와 패턴 인식 기법에 의한 부분방전 해석

  • Published : 2006.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, we detected electromagnetic waves generated in an enclosed switchgear and applied various statistical methods for detecting signals. We calculated the various statistical factors via the appropriate statistical methods. Further, we used these statistics to recognize the characteristics for each pattern by identifying the partial discharge in each case for normal, proceeding and abnormal states. The characteristics of electromagnetic wave patterns occurred in various states at electric power facilities and were used as an output variable for more efficient diagnosis. In this paper, we confirmed that the pattern of partial discharge signal can be used as one of the factors used to analyze the insulation state and to consider while estimating diagnosis of insulation states by recognizing the signal pattern to intelligence. We will utilize the proposed diagnosis method to determine insulation degradation states.

Keywords

References

  1. F. H. Kreuger, Partial Discharge Detection in High-voltage Equipment, Butterworths, High-voltage engineering at the technical university - Delft, 1989
  2. S. Tetrault, G. C. Stone, and H. G. Sedding, 'Monitoring partial discharges on 4 kV motor windings,' IEEE Trans. on Industry Applications, vol. 35, Issue 3, pp. 682-688, May-June 1999 https://doi.org/10.1109/28.767022
  3. A. Golubev, I. Blokhintsev, G. Paoletti, and J. Modrowski, 'Online partial discharge applications to MV electrical switchgear,' Proc. of the Electrical Insulation Conference and Electrical Manufacturing & Coil Winding Conference 2001, pp. 225-232, October 2001 https://doi.org/10.1109/EEIC.2001.965615
  4. E. Carminati, L. Cristaldi, M. Lazzaroni, and A. Monti, 'Partial discharge mechanism detection by neuro-fuzzy algorithm,' IEEE Instrumentation and Measurement Technology Conference 1998, pp. 744-748, May 1998 https://doi.org/10.1109/IMTC.1998.676824
  5. M. M. A. Salama, 'Determination of neural network topology for partial discharge pulse pattern recognition,' IEEE Trans. on neural networks, vol. 13, no. 2, pp. 446-456, 2002 https://doi.org/10.1109/72.991430
  6. Y. Li, F. Lu, B. Xin, and Z. Chen, 'A new method using ultrasonic for partial discharge pattern recognition,' Proc. of international Conference on Power System Technology 2002, vol. 2, pp. 13-17, October 2002 https://doi.org/10.1109/ICPST.2002.1047550
  7. Y. Cheng, X. Chen, B. Yue, and H. Xie, 'Field detection of ultra-wideband partial discharge of generator stator insulation,' Proc. of international Conference on Power System Technology 2002, vol. 3, pp. 1847-1850, October 2002 https://doi.org/10.1109/ICPST.2002.1067851
  8. Q. Chen, Y. Wang, and X. Wie, 'The UHF method for measurement of partial discharge and interface suppression,' Proc. of International Symposium on Electrical Insulating Materials 2005, vol. 3, pp. 829-832, June 2005
  9. Y. Ming and S. Birlasekaran, 'Characterization of partial discharge signals using wavelet and statistical technique,' Conference Record of the 2002 IEEE International Symposium on Electrical Insulation, pp. 9-13, April 2002 https://doi.org/10.1109/ELINSL.2002.995869
  10. J. S. Oh, J. B. Park, and Y. H. Choi, 'Stable path tracking control of a mobile robot using a wavelet based fuzzy neural network,' International Journal of Control, Automation, and Systems, vol. 3, no. 4. pp. 552-563. December 2005
  11. M.-H. Chang, M.-S. Oh, C.-H. Lim, M. B. Ahmad, and J.-A. Park, 'Facial recognition algorithm based on edge detection and discrete wavelet transform,' Trans. on Control, Automation, and Systems Engineering, vol. 3, no. 4, pp. 283-288, December 2001
  12. K.-T. Oh and S.-G Lee, 'A defect inspection method of the IH-JAR by statistical pattern recognition,' Journal of Control, Automation and System Engineering, vol. 6, no. 1, pp. 112-119, January 2000
  13. T. S. Yoon, 'Parity space and pattern recognition approach for hardware redundant system signal validation using artificial neural networks,' Journal of Control, Automation and Systems Engineering, vol. 4, no. 6, pp. 765-771, December 1998