Proceedings of the KIPE Conference (전력전자학회:학술대회논문집)

The Korean Institute of Power Electronics (전력전자학회)
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A Power Disturbance Classification System using Wavelet-Based Neural Network

웨이블릿 기반의 뉴럴네트웍을 이용한 전원의 왜란분류 시스템

  • Kim, Hong-Kyun (School of Electrical and computer Engineering, Chungbuk National University) ;
  • Lee, Jin-Mok (School of Electrical and computer Engineering, Chungbuk National University) ;
  • Choi, Jae-Ho (School of Electrical and computer Engineering, Chungbuk National University)
  • 김홍균 (충북대학교 전기전자 컴퓨터공학부) ;
  • 이진목 (충북대학교 전기전자 컴퓨터공학부) ;
  • 최재호 (충북대학교 전기전자 컴퓨터공학부)
  • Published : 2005.07.04
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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