DOI QR코드

DOI QR Code

PD Classification by Neural Networks in Specimen of XLPE Power Cable

XLPE 전력용 케이블 시편의 부분방전원 분류

  • 박성희 (충북대학교 전기전자컴퓨터공학부) ;
  • 이강원 (한국철도기술연구) ;
  • 강성화 (충청대학 산업안전) ;
  • 임기조 (충북대학교 전기전자컴퓨터공학부)
  • Published : 2004.08.01

Abstract

In this paper, neural networks is studied to apply as a PD source classification in XLPE power cable specimen. For treeing discharge sources in the specimen, three defected models are made. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different models of discharge sources. And also these parameter is applied to classify PD sources by neural networks. Neural Networks has good recognition rate for three PD sources.

References

  1. 기술교육교재 전력기기 절연 진단 기술 한국전기연구원
  2. IEEE Trans. on EI v.28 no.6 Classification of partial discharge F. H. Kreuger;E, Gulski;A. Krivda
  3. IEEE Trans. on EI v.27 no.1 Computer-aided recognition of discharge sources E. Gulski;F. H. Kreuger
  4. IEEE Trans. on EI v.27 no.1 The importance of statistical charcteristics of partial discharge data B. Fruth;L. Niemer
  5. IEEE Trans. on EI v.27 no.3 pattern recognition of partial discharges in xple cables using a neural networks H. Suzuki;T. Endoh
  6. IEEE Trans. on NN v.13 no.2 Determination of neural network topology for partial discharge pulse pattern recognition M. M. A. Salama;R. Bartnikas https://doi.org/10.1109/72.991430
  7. IEEE Trans. on EI v.28 no.6 neural network as a tool for recognition of partial discharge E. Gulski;A. Krivda
  8. 전기전자재료학회논문지 v.17 no.1 STFT 및 통계적 처리에 의한 공기 중 부분방전원 식별 이강원;박성희;강성화;임기조