A Study on Diagnosis of Transformers Aging Sate Using Wavelet Transform and Neural Network

이산웨이블렛 변환과 신경망을 이용한 변압기 열화상태 진단에 관한 연구

  • 박재준 (중부대학교 정보공학부) ;
  • 송영철 (광운대학교 전기공학과) ;
  • 전병훈 (중부대학교 정보공학부)
  • Published : 2001.01.01

Abstract

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.

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