Browse > Article

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

Lee, Dae-Jong (충북대학교 BK21충북정보기술사업단)
Lee, Jong-Pil (충북대학교 전기공학과)
Ji, Pyeong-Shik (충북대학교 전기공학과)
Lim, Jae-Yoon (대덕대학 전기과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.56, no.6, 2007 , pp. 1007-1016 More about this Journal
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
FCM;
Citations & Related Records

Times Cited By SCOPUS : 0
연도 인용수 순위
  • Reference
1 Bezdec, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981
2 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   DOI   ScienceOn
3 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   DOI   ScienceOn
4 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   DOI   ScienceOn
5 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   DOI   ScienceOn
6 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
7 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   DOI   ScienceOn
8 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   DOI
9 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   DOI   ScienceOn
10 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   DOI   ScienceOn
11 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   DOI   ScienceOn
12 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   DOI