전력용 변압기의 유중가스 분석을 위한 LVQ3의 적용

Application of LVQ3 for Dissolved Gas Analysis for Power Transformer

  • 발행 : 2000.01.01

초록

To enhance the fault diagnosis ability for the dissolved gas analysis(DGA) of the power transformer, this paper proposes a learning vector quantization(LVQ) for the incipient fault recognition. LVQ is suitable expecially for pattern recognition such as fault diagnosis of power transformer using DGA because it improves the performance of Kohonen neural network by placing emphasis on the classification around the decision boundary. The capabilities of the proposed diagnosis system for the transformer DGA decision support have been extensively verified through the practical test data collected from Korea Electrical Power Corporation.

키워드

참고문헌

  1. C.E. Lin, J.M. Ling, and C.L. Huang, 'An Expert System for Transformer Fault Diagnosis Using Dissolved Gas Analysis,' IEEE Transactions on Power Delivery, Vol. 8, No. 1, pp.231-238, January 1993 https://doi.org/10.1109/61.180341
  2. K. Tomsovic, M. Tapper, and T. Ingvarsson, 'A Fuzzy information Approach to Integrating Different Transformer Diagnostic Methods,' IEEE Transactions on Power Delivery, Vol 8, No. 3, pp. 1638-1646, July 1993 https://doi.org/10.1109/61.252690
  3. Y. Zhang, X. Ding, Y. Lie, and P. J. Griffin, 'An Artificial Neural Network Approach to Transformer Fault Diagnosis,' IEEE Transactions on Power Delivery, Vol 11, No. 4, pp. 1836-1841, October 1996 https://doi.org/10.1109/61.544265
  4. 윤용한, 전영재, 김재철, '유중가스 분석을 이용한 전력용 변압기 고장진단용 코호넨 네트워크,' 대한전기학회, 제 47권, 6호, pp. 741-745- 1998년 6월
  5. Jae-Chul Kim, Yong-Han Yoon, Do-Hyuk Choi, Young-Jae Jeon, 'A Kohonen Neural Network Approach for Transformer Fault Diagnosis using Dissolved Gas Analysis,' Proceeding of the International Conference on Intelligent System Application to Power System at Seoul, Korea, pp. 336-340, July 1997
  6. Y.C. Huang, H.T. Yang, and C.L. Huang, 'Developing a new transformer fault diagnosis system through evolutionary fuzzy logic,' IEEE Transaction on Power Delivery, Vol 12, No. 2, pp. 761-767, 1997 https://doi.org/10.1109/61.584363
  7. Zhenyanuan Wang, Yilu Liu, and Paul J. Griffin, 'A Combined ANN and Expert System Tool for Transformer Fault Diagnosis,' IEEE Transaction on Power Delivery, Vol. 13, No. 4, pp. 1224-1229, October 1998 https://doi.org/10.1109/61.714488
  8. R.R. Rogers, 'IEEE and IEC Codes to Interpret Incipient Faults in Transformers Using Gas in Oil Analysis,' IEEE Transaction on Electrical Insulation, Vol 13, No. 5, pp. 349-354, October 1978 https://doi.org/10.1109/TEI.1978.298141
  9. IEC Publication 599-1978, Interpretation of The Analysis of Gases in Transformers and Other Oil-Filled Electrical Equipment in Service
  10. J.J. Kelly, 'Transformer Fault Diagnosis by Dissolved Gas Analysis', IEEE Transactions on Industry Application, Vol. 16, No. 6, pp. 777-782, November 1980
  11. 남창현, 이태원, 원도영, '변압기 유중 가스 상시 감시 시스템의 운용 연구(연구 보고서), 전력연구원, 1995
  12. T. Kohonen, 'Self-Organization and Associative Memory,' (3rd ed), 1989
  13. T, Kohonen, 'Statistical Pattern Recognition revisited,' Advanced Neural Computers,' pp. 137-144, 1990
  14. T. Kohonen, 'LVQ PAK - The Learning Vector Quantization Program Package,' Helsinki University of Technology, Finland, 1992