Journal of the Korean Institute of Electrical and Electronic Material Engineers (한국전기전자재료학회논문지)

The Korean Institute of Electrical and Electronic Material Engineers (한국전기전자재료학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Pattern Making of Degradation Type Using K-means

K-means를 이용한 열화 형태의 패턴화에 관한 연구

  • Lee, Deok-Jin (Department of Ubiquitous IT, FAR-EAST University)
  • 이덕진 (극동대학교 유비쿼터스IT학과)
  • Received : 2014.11.05
  • Accepted : 2014.11.18
  • Published : 2014.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

It has been confirmed that the inner defect of transformer and the perfect diagnosis for aging are closely related to safe electric power transmission system and that the detection of accident and diagnosis technique turn out to be very important issues. Since electric power machinery consists of various kinds of components, however, it is very difficult to make a diagnosis for aging by one parameter. Thus, diagnosis for aging is feasible only through the combination of various parameters. Recently, various expert systems have been developed and applied to diagnosis for aging, but they are not yet reliable enough to apply to the real system. In this paper, XLPE which is ultra high voltage cable insulator material were chosen to investigate the influence of void on insulator material using partial discharge. Obtained data have been processed by PRPD (phased resolved partial discharge) distribution function and K-means. And statistical and cluster distribution of partial discharge have been analysed and investigated.

Keywords

References

  1. T. Y. Kim and J. W. Hong, KIEE International Transactions on Electrophysics and Applications, 5-C, 155 (2005).
  2. E. Kuffel and W. S. Zaengl, Pergamon Press, 1 (1970).
  3. G. Wu, X. Jiang, H. Xie, and D. H. Park, Proc. of The 6th ICPADM, 558 (2000).
  4. T. Shimakage, K. Wu, T. Okamoto, and Y. Suzuoki, IEEJ Trans. FM., 124, 277 (2004). https://doi.org/10.1541/ieejfms.124.277
  5. K. Uchida, T. Hirata, and H. Takehana, T. IEE Japan, 115-B, 1185 (1995).
  6. J. B. MacQueen, Proc. of Mathematical Statistics and Probability (University of California Press, 1967). p. 281.
  7. P. Drineas, A. Frieze, R. Kannan, S. Vempala, and V. Vinay, Machine Learning, 56, 9 (2004). https://doi.org/10.1023/B:MACH.0000033113.59016.96
  8. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, Proc. of KDD, 226 (1996).
  9. M. Ankerst, M. M. Breunig, H. P. Kriegel, and J. Sander, Proc. of ACM SIGMOD, 28, 49 (1999).
  10. F. Hoppner, F. Klawonn, and R. Kruse, Thomas Runkler (John Wiley & Sons Ltd., 1999). p. 1-59.