A Study on Feature Extraction of Partial Discharge Type Using Wavelet Transform

웨이블렛변환을 이용한 부분방전 종류의 특징추출에 관한 연구

  • Park, Jae-Jun (Division of Information Engineering, Joongbu Univ.)
  • 박재준 (중부대학교 정보공학부 전기.전자공학)
  • Published : 2003.03.01

Abstract

In this papers, we proposed the new method in order to diagnosis partial discharge type of transformers. For wavelet transform, Daubechie's filter is used,, we can obtain wavelet coefficients which is used to extract featrue of statistical parameters(maximum value, average value, dispersion, skewness, kurtosis) about high frequency current signal per 3-electrode type(needle-plane electrode, IEC electrode and Void electrode). Also, these coefficients are used to identify signal of internal partial discharge in transformer. As a result, from compare of high frequency current signal amplitude and average value, we are obtained results of IEC electrode> Void electrode> Needle-Plane electrode. Otherwise, in case of skewness and kurtosis, we are obtained results of Void electrode> IEC electrode> Needle-Plane electrode. As improved method in order to diagnosis partial discharge type of transformers, we use neural network.

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