Fault Diagnosis of Power Transformer by FCM and Euclidean Based Distance Measure

FCM과 유클리디언 기반 거리유사도에 의한 전력용 변압기의 고장진단

  • 이대종 (충북대학교 BK21충북정보기술사업단) ;
  • 이종필 (충북대학교 전기공학과) ;
  • 지평식 (충북대학교 전기공학과) ;
  • 임재윤 (대덕대학 전기과)
  • Published : 2007.06.01

Abstract

In power system, substation facilities have become too complex and larger according to an extended power system. Also, customers require the high quality of electrical power system. However, some facilities become old and often break down unexpectedly. The unexpected failure may cause a break in power system and loss of profits. Therefore it is important to prevent abrupt faults by monitoring the condition of power systems. Among the various power facilities, power transformers play an important role in the transmission and distribution systems. In this research, we develop intelligent diagnosis technique for predicting faults of power transformer by FCM(Fuzzy c-means) and Euclidean based distance measure. The proposed technique make it possible to measures the possibility and degree of aging as well as the faults occurred in transformer. To demonstrate the validity of proposed method, various experiments are performed and their results are presented.

Keywords

References

  1. Pyeong Shik Ji, Jae Yoon Lim, Jong Pil Lee, 'Aging characteristics of power transformer oil and development of its analysis using KSOM', TENCON 99, Proceedings of the IEEE Region, Vol. 2, pp. 1026-1029, 1999 https://doi.org/10.1109/TENCON.1999.818596
  2. Magn-Hui Wang, Hong-Chan Chang, 'Novel clustering method for coherency identification using an artificial neural network,' IEEE Transaction on Power Systems, vol. 9, Nov. pp. 2056-2062, 1994 https://doi.org/10.1109/59.331469
  3. J. L. Naredo, P. Moreno, C. R. Fuerte, 'A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis,' IEEE Transaction on Power Delivery, Vol.16, pp. 643-647, 2001 https://doi.org/10.1109/61.956751
  4. V. Miranda, A. R G. Castro, 'Improving the IEC table for transformer failure diagnosis with knowledge extraction from neural networks,' IEEE Transaction on Power Delivery, Vol. 20, pp. 2509-2516, 2005 https://doi.org/10.1109/TPWRD.2005.855423
  5. Hong-Tzer Yang; Chiung-Chou Liao, 'Adaptive fuzzy diagnosis system for dissolved gas analysis of power transformers,' IEEE Transaction on Power Delivery, Vol. 14, pp. 1342-1350, 1999 https://doi.org/10.1109/61.796227
  6. Z. Yan, M. Dong, Y. Shang, M. Muhr, 'Ageing Diagnosis and Life Estimation of Paper Insulation for Operating Power Transformer', International Conference on Solid Dielectrics, Vol. 2, pp. 715-718, 2004 https://doi.org/10.1109/ICSD.2004.1350531
  7. Zhenyuan Wang, Yilu Liu, Paul J. Griffin, 'A Combined ANN and Expert System Tool for Transformer Fault Diagnosis', IEEE Transactions on Power Delivery, Vol. 13, No.4, pp. 1224-1229, 1998 https://doi.org/10.1109/61.714488
  8. Ganyun Kv, Haozhong Cheng, Haibao Zhai, Lixin Dong, 'Fault diagnosis of power transformer based on multi-layer SVM classifier', Electric Power System Research, Vol. 75, pp. 9-15, 2005 https://doi.org/10.1016/j.epsr.2004.07.013
  9. Q. Su, L. L. Lai, P. Austin, 'A Fuzzy Dissolved Gas Analysis Method for the Diagnosis of Multiple Incipient in a Transformer', International Conference on Advances in Power System Control, Operation and Management(APSCOM), pp. 344-348, 2000
  10. K. F. Thang, R. K. Aggarwal, A. l. McGrail. D. G. Esp, 'Analysis of Power Transformer Dissolved Gas Data Using the Self-Organizing Map', IEEE Transactions on Power Delivery, Vol. 18, No.4, pp. 1241-1248, 2003 https://doi.org/10.1109/TPWRD.2003.817733
  11. H. T.Yang et aI, 'Intelligent Decision Support for Diagnosis of Incipient Transformer Faults Using Self-Organizing Polynominal Networks', IEEE Trans., Power System, Vol. 13, No.3, pp. 946-952, 1998 https://doi.org/10.1109/59.708845
  12. Bezdec, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981