한국전기전자재료학회논문지 (Journal of the Korean Institute of Electrical and Electronic Material Engineers)

  • 제29권2호
  • /
  • Pages.114-119
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  • 2016
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  • 1226-7945(pISSN)
  • /
  • 2288-3258(eISSN)
한국전기전자재료학회 (The Korean Institute of Electrical and Electronic Material Engineers)
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음향신호의 유중 전파특성 분석

Analysis of Propagation Characteristics of Acoustic Signal in Insulation Oil

  • 윤민영 (한국해양대학교 전기전자공학과) ;
  • 박경수 (한국해양대학교 전기전자공학과) ;
  • 왕국명 (한국해양대학교 전기전자공학과) ;
  • 김선재 (한국해양대학교 전기전자공학과) ;
  • 길경석 (한국해양대학교 전기전자공학과)
  • Yun, Min-Young (Department of Electrical and Electronics Engineering, Korea Maritime and Ocean University) ;
  • Park, Kyoung-Soo (Department of Electrical and Electronics Engineering, Korea Maritime and Ocean University) ;
  • Wang, Guoming (Department of Electrical and Electronics Engineering, Korea Maritime and Ocean University) ;
  • Kim, Sun-Jae (Department of Electrical and Electronics Engineering, Korea Maritime and Ocean University) ;
  • Kil, Gyung-Suk (Department of Electrical and Electronics Engineering, Korea Maritime and Ocean University)
  • 투고 : 2016.01.19
  • 심사 : 2016.01.23
  • 발행 : 2016.02.01
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초록

This paper dealt with the propagation characteristics of acoustic signal in insulation oil for the purpose of improving the reliability of AE (acoustic emission) method used for condition monitoring of oil-immersed transformers. A discharge source was placed in insulation oil and AE sensors ($f_c$ :140 kHz) were attached on the oil tank to study the changes of velocity and propagation path with the depth and distance. The average velocity was 1,436 m/s and the velocity decreased with the increase of depth from the oil surface to 430 mm. The propagation paths were classified into three sections by the shortest reflection path of the detected signal. The minimum distinguishable distance in each section was 70 mm. It was also verified that PD (partial discharge) with a magnitude over than 500 pC can be detected by the AE sensors.

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참고문헌

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피인용 문헌

  1. Power Transformer Partial Discharge Fault Diagnosis Based on Multidimensional Feature Region vol.2016, 2016, https://doi.org/10.1155/2016/4835694