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Electrical Arc Detection using Artificial Neural Network

인공 신경망을 이용한 전기 아크 신호 검출

  • Lee, Sangik (Electrical Safety Research Institute & Korea Electrical Safety Corp.) ;
  • Kang, Seokwoo (Electrical Safety Research Institute & Korea Electrical Safety Corp.) ;
  • Kim, Taewon (Electrical Safety Research Institute & Korea Electrical Safety Corp.) ;
  • Lee, Seungsoo (Dept. of Computer & Communications Eng., Kangwon National University) ;
  • Kim, Manbae (Dept. of Computer & Communications Eng., Kangwon National University)
  • 이상익 (한국전기안전공사 전기안전연구원) ;
  • 강석우 (한국전기안전공사 전기안전연구원) ;
  • 김태원 (한국전기안전공사 전기안전연구원) ;
  • 이승수 (강원대학교 컴퓨터정보통신공학과) ;
  • 김만배 (강원대학교 컴퓨터정보통신공학과)
  • Received : 2019.05.07
  • Accepted : 2019.07.31
  • Published : 2019.09.30

Abstract

The serial arc is one of factors causing electrical fires. Over past decades, various researches have been carried out to detect arc occurrences. Even though frequency analysis, wavelet and statistical features have been used, arc detection performance is degraded due to diverse arc waveforms. Therefore, there is a need to develop a method that could increase the feature dimension, thereby improving the detection performance. In this paper, we use variational mode decomposition (VMD) to obtain multiple decomposed signals and then extract statistical features from them. The features from VMD outperform those from no-VMD in terms of detection performance. Further, artificial neural network is employed as an arc classifier. Experiments validated that the use of VMD improves the classification accuracy by up to 4 percent, based on 14,000 training data.

전기화재의 원인중의 하나는 직렬 아크이다. 최근까지 아크 신호를 검출하기 위해 다양한 기법들이 진행되고 있다. 시간 신호에 푸리에 변환, 웨이블릿, 또는 통계적 특징 등을 활용하여 아크 검출을 하는 방법들이 소개되었지만, 다양한 불규칙 아크 파형 때문에, 실제 환경에서는 아크 성능이 저하되는 문제가 있다. 따라서, 기존의 부족한 특징 데이터를 증가시켜, 성능을 개선하는 것이 요구된다. 본 논문에서는 입력신호를 변분 모드 분할을 통해 원신호를 분할한 후 통계적 특징을 추출한다. 변분 모드 분할으로부터 추출한 통계적 특징의 성능이 원신호로부터 얻은 특징보다 개선된 성능을 얻는다. 아크 분류기로 인공 신경망을 이용하고, 14,000개의 학습 데이터에 적용한 결과 VMD의 사용이 약 4%의 아크 검출 성능을 높혔다.

Keywords

References

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