대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 1997년도 하계학술대회 논문집 D
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- Pages.897-899
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- 1997
신경망과 카오스 현상을 이용한 고저항 지락 사고 검출 기법에 관한 연구
A Study on High Impedance Fault Defection Method Using Neural Nets and Chaotic Phenoma
- 유창완 (광운대학교 제어계측 공학과) ;
- 심재철 (광운대학교 제어계측 공학과) ;
- 고재호 (광운대학교 제어계측 공학과) ;
- 배영철 (산업기술정보원) ;
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임화영
(광운대학교 제어계측 공학과)
- Ryu, Chang-Wan (Dept. of Control & Inst. Eng. Kwongwoon Univ.) ;
- Shim, Jae-Chul (Dept. of Control & Inst. Eng. Kwongwoon Univ.) ;
- Ko, Jae-Ho (Dept. of Control & Inst. Eng. Kwongwoon Univ.) ;
- Bae, Young-Chul (KINITI) ;
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Yim, Wha-Yeong
(Dept. of Control & Inst. Eng. Kwongwoon Univ.)
- 발행 : 1997.07.21
초록
The analysis of distribution line faults is essential to the proper protections of the power system. A high impedance fault does not make enough current to cause conventional protective devices. It is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. This paper describes an algorithm using back-propagation neural network for pattern recognition and detection of high impedance faults. Fractal dimensions are estimated for distinction between random noise and chaotic behavior in the power system. The fractal dimension of the line current is also used as a indication of the high impedance fault.
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