한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제26권5호
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  • Pages.409-415
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  • 2016
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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전기부하 패턴분류를 위한 신호처리 기법에 관한 연구

A Study on the Signal Processing Techiques for Pattern Classification of Electrical Loads

  • 임용배 (한국전기안전공사 전기안전연구원) ;
  • 김동우 (한국전기안전공사 전기안전연구원) ;
  • 진상민 (홍익대학교 전자전기공학부) ;
  • 조성원 (홍익대학교 전자전기공학부)
  • Lim, Young Bae (Electrical Safety Research Institute, a subsidiary of Korea Electrical Safety Corporation) ;
  • Kim, Dong Woo (Electrical Safety Research Institute, a subsidiary of Korea Electrical Safety Corporation) ;
  • Jin, Sangmin (School of Electrical and Electronics Engineering, Hongik University) ;
  • Cho, Seongwon (School of Electrical and Electronics Engineering, Hongik University)
  • 투고 : 2016.09.05
  • 심사 : 2016.10.13
  • 발행 : 2016.10.25
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초록

최근 사물인터넷 기반의 재해예방 기술이 개발되고 있다. 본 논문에서는 사물인터넷기반의 공동주택용 자율전기안전관리 기술 개발을 위하여 부하 전류 파형을 FFT와 MFCC를 이용하여 신호변환 후 신경회로망 모델에 적용하여 정확도가 개선된 전기 부하 패턴분류 시스템을 제안한다. 오실로스코프와 CT를 이용하여 측정한 전기 부하의 전류 파형을 FFT 알고리즘을 적용한 후 신경회로망을 이용하여 단일부하패턴 분류 실험을 하였다. 본 연구를 통하여 부하의 특성을 파악함으로서 고장에 대해 보다 신속하고 정확하게 대처할 수 있을 것으로 예측된다.

Recently several techniques for disaster prevention based on IoT(Internet of Things) are being developed. In this paper, a new smart pattern classification method for electric loads is proposed. CT(Current Transformer) data are extracted from electric loads, and then the sampled CT data are converted using FFT and MFCC. FFT and FMCC data are used for the input data of neural networks. Experiments were conducted using FFT and MFCC data for 7 kinds of electric loads. Experiments results indicate the superiority of MFCC in comparison to FFT.

키워드

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