A Study on Feature Extraction of Transformers Aging Signal using discrete Wavelet Transform Technique

이산 웨이블렛 변환 기법을 이용한 변압기 열화신호의 특징추출에 관한 연구

  • 박재준 (중부대 리공대 정보공학부) ;
  • 권동진 (한국전력공사 전력연구원) ;
  • 송영철 (광운대 공과대 전기공학과) ;
  • 안창범 (광운대 공과대 전기공학과)
  • Published : 2001.03.01

Abstract

In this paper, a new efficient feature extraction method based on Daubechies discrete wavelet transform is presented. This paper especially deals with the assessment of process statistical parameter using the features extracted from the wavelet coefficients of measured acoustic emission signals. Since the parameter assessment using all wavelet coefficients will often turn out leads to inefficient or inaccurate results, we selected that level-3 stage of multi decomposition in discrete wavelet transform. We make use of the feature extraction parameter namely, maximum value of acoustic emission signal, average value, dispersion, skewness, kurtosis, etc. The effectiveness of this new method has been verified on ability a diagnosis transformer go through feature extraction in stage of aging(the early period, the middle period, the last period)

Keywords

Acoustic Emission Signals;Discrete Wavelet Transform;FIR Digital Filter;Feature Extraction;Multi-Decomposition

References

  1. William H.Press .Numerical Recipes in C 'The art of scientific computing second edition',Cambridge University Press, 1992
  2. Andrew Bruce, David Donho & Hong-Ye Gao ,' Wavelet Analysis ', IEEE SPECTRUM, pp26-35,1996
  3. W. Awilkinson, M.D.Cox, 'Discrete Wavelet Analysis of Power System Transients',IEEE Transaction on Power System, Vol .11,No4,1996 https://doi.org/10.1109/59.544682
  4. Masahiro KOZAKO, Zhihai TIAN, Hitoshi OKUBO, Nobuyuki SHIBATA, Masayuki HIKITA, 'Noise Reduction and PD Measurements Using Digital Filter and Signal Processing Technique in HV Substations' Proceeding of 1998 International Symposium on Electrical Insulating Materials,E2-2,pp561-564,1998 https://doi.org/10.1109/ISEIM.1998.741805
  5. Akram Al-Rawi, Michael Devaney, 'Wavelet and Power System Transient Analysis', Proceeding of the IEEE Instrumentation and Measurement Technology Conference, Vol 2,pp1331-1334,1998 https://doi.org/10.1109/IMTC.1998.676968
  6. Hang Wang ,Karen L.Butler, 'Detection of Transformer Winding Faults Using Wavelet Analysis and Neural Network', Intelligent System Application to Power Systems(ISAP'99)April 4 -8,pp 231-235, 1999
  7. Agostino Abbate, Jeff Koay, Julius Frankel, Stephan C. Schroeder, and Pankaj, 'Signal Detection and Noise Suppression Using a Wavelet Transform Signal Processor : Application to Ultrasonic Flaw Detection' IEEE Transaction on Ultrasonic Flaw Detection' IEEE Transaction on Ultrasonics, Ferroelectrics, and Frequency Control,Vol 44,No.1,1997 https://doi.org/10.1109/58.585186
  8. Stefan Pittner and Sagar V.Kamarthi, 'Feature Extraction from Wavelet Coefficients for Pattern Recognition Tasks' ,IEEE Transaction on Pattern Analysis and Machine Intelligence,Vol.21,No.1,pp83-88,1999 https://doi.org/10.1109/34.745739
  9. Mladen Victor Wickerhauser , 'Adapted Wavelet Analysis from Theory to Software', IEEE PRESS, pp213-234,1994
  10. A Primer, 'Introduction to Wavelets and Wavelet Transforms', Prentice Hall ,pp1-38,1998
  11. Gilbert Strang, Truong Nguyen, 'Wavelet and Filter Banks' , Wellesley-Cambridge Press, 1996
  12. 박 재 준,' 음향방출 계측법을 이용한 트리잉 열화진단에 관한 연구' 광운대학교 박사학위논문, 1993
  13. T. Okamoto and T. Tanaka, ' Cycle-mean $\phi$ - q characteristic of partial discharges in six electrode system', JIEE Vol. 102, No.7, pp. 7-14, 1982
  14. Xiaoli Li, Shen Dong, Zhejun Yuan, 'Discrete Wavelet Transform for Tool Breakage Monitoring', International Journal of Machine Tools & Manufacture 1935-1944 ,1999 https://doi.org/10.1016/S0890-6955(99)00021-8
  15. Santosh Kumar Pandey and L. Satish , 'Multiresolution Signal Decomposition : A New Tool For Fault Detection in Power Transformers During Impulse Tests', IEEE Transaction on Power Delivery ,Vol.13, No.4 , pp1194-1200, 1998 https://doi.org/10.1109/61.714484