Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)

The Korean Institute of Electrical Engineers (대한전기학회)
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A Wavelet-Based Neural Network System for Power Disturbance of Recognition and Classification

전원왜란의 인지와 분류를 위한 웨이블릿을 기반으로한 뉴럴네트웍 시스템

  • Kim, Hong-Kyun (School of Electrical and Computer, Chungbuk National University) ;
  • Lee, Jin-Mok (School of Electrical and Computer, Chungbuk National University) ;
  • Choi, Jea-Ho (School of Electrical and Computer, Chungbuk National University)
  • 김홍균 (충북대학교 전기전자 컴퓨터공학부) ;
  • 이진목 (충북대학교 전기전자 컴퓨터공학부) ;
  • 최재호 (충북대학교 전기전자 컴퓨터공학부)
  • Published : 2005.07.18
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Abstract

This paper presents a wavelet-based neural network technology for the detection and classification of the short durations type of power quality disturbances. Transients happen during very short durations to the nano- and microsecond. Thus, a method for detecting and classifying transient signals at the same time and in an automatic combines the properties of the wavelet transform and the advantages of neural networks. Especially, the additional feature extraction to improve the recognition rate is considered. The configuration of the hardware of TMS320C6711 DSP based with 16 channel 20Mhz sampling rate A/D(Analog to Digital) converter and some case studies are described.

Keywords