A Study on the Pattern Recognition Rate of Partial Discharge in GIS using an Artificial Neural Network

  • Kang Yoon-Sik (Power Testing & Technology Institute, LS Industrial Systems) ;
  • Lee Chang-Joon (Power Testing & Technology Institute, LS Industrial Systems) ;
  • Kang Won-Jong (Power Testing & Technology Institute, LS Industrial Systems) ;
  • Lee Hee-Cheol (Power Testing & Technology Institute, LS Industrial Systems) ;
  • Park Jong-Wha (Power Testing & Technology Institute, LS Industrial Systems)
  • 발행 : 2005.04.01

초록

This paper describes analysis and pattern recognition techniques for Partial Discharge(PD) signals in Gas Insulated Switchgears (GIS). Detection of PD signals is one of the most important factors in the predictive maintenance of GIS. One of the methods of detection is electro magnetic wave detection within the Ultra High Frequency (UHF) band (300MHz $\~$ 3GHz). In this paper, PD activity simulation is generated using three types of artificial defects, which were detected by a UHF PD sensor installed in the GIS. The detected PD signals were performed on three-dimensional phi-q-n analysis. Finally, parameters were calculated and an Artificial Neural Network (ANN) was applied for PD pattern recognition. As a result, it was possible to discriminate and classify the defects.

키워드

참고문헌

  1. Sander Meijer, 'Partial Discharge Diagnosis of Highvoltage Gas-Insulated Systems', Optima Grafische Communicatie Rotterdam, ISBN 90-77017-23-2
  2. Nicholas de kock, Branko Coric and Ralf Pietsch, 'UHF PD detection in gas insulated switchgearsuitability and sensitivity of the UHF method in comparison with the IEC 270 method', IEEE Electrical Insulation Magazine, Vol. 12, No. 6, pp. 20-26, 1996 https://doi.org/10.1109/57.546277
  3. S. Meijer, E. Gulski, J. J. Smit, 'Pattern analysis of Partial Discharges in SF6 GIS', IEEE Transactions on Dielectrics and Electrical Insulation, Vol. 5, No. 6, pp. 830-842, Dec., 1998 https://doi.org/10.1109/94.740764
  4. Anil K, Jianchang Mao, K. M. Mohiuddin, 'Artificial Neural Networks: A Tutorial', IEEE, Mar., 1996
  5. Minsky and Papert, 'Perceptrons', MIT press, Cambridge, 1969
  6. J. G. Choi, S. H. Yi, K. H. Kim, I. S. Kim and J. C. Kim, 'Detection Characteristics of a Novel Coupler for GIS PD Detection,' KIEE Int. Trans. On EA, Vol. 3-C, No.6, 2003
  7. Peng Yuan, 'An automated recognition system of ultrahigh-frequency PD in transformers', 2002 Annual Report Conference on Electrical Insulation and Dielectric Phenomena, pp. 514-517
  8. K. W. Lee, K. J. Lim and S. H. Kang, 'Application of RBFN using LPC of PD Pulse Shapes for Discriminating Among Multi PD Sources,' KIEE Int. Trans. On EA, Vol.3-C, No.5, 2003
  9. M. Oyama, E. Hanai, H. Aoyagi, 'Development of detection and diagnostic techniques for partial discharges in GIS', IEEE Transactions on Power Delivery, Vol. 9, No. 2, pp. 811-818, April 1994 https://doi.org/10.1109/61.296261