The Transactions of the Korean Institute of Electrical Engineers C (대한전기학회논문지:전기물성ㆍ응용부문C)

The Korean Institute of Electrical Engineers (대한전기학회)
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Discrimination of Air PD Sources Using Time-Frequency Distributions of PD Pulse Waveform

부분방전 펄스파형의 시간-주파수분포를 이용한 기중부분방전원의 식별

  • Published : 2005.07.01
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Abstract

PD(Partial Discharge) signal emitted from PD sources has their intrinsic features in the region of time and frequency STFT(Short Time Fourier Transform) shows time-frequency distribution at the same time. 2-Dimensional matrices(33$\times$77) from STFT for PD pulse signals are a good feature vectors and can be decreased in dimension by wavelet 2D data compression technique. Decreased feature vectors(13$\times$24) were used as inputs of Back-propagation ANN(Artificial Neural Network) for discrimination of Multi-PD sources(air discharge sources(3), surface discharge(1)). They are a good feature vectors for discriminating Multi-PD sources in the air.

Keywords

References

  1. IEC Standard 60270, Partial Discharge Measurements, pp. 1-57, 1980
  2. F. H. Kreuger, E. Gulski and A. Krivda, 'Classification of Partial Discharge', IEEE Trans, Electrical Insulation, Vol. 28, No. 6, pp. 917-931, 1993 https://doi.org/10.1109/14.249365
  3. L. E. Lundgaard, et al 'Acoustic Diagnosis of Gas Insulated Substation; A Theoretical and Experimental Basis' IEEE Transactions on Power Delivery, Vol.5, No.4, November 1990 https://doi.org/10.1109/61.103670
  4. Brian Hampton 'UHF Diagnostics For Gas Insulated Substations'; High Voltage Engineering Symposium, IEE No.467, 22-27 August 1999
  5. E. Gulski, and A. Krivda, 'Neural Networks as a Tool for Recognition of Partial Discharges', IEEE Trans. On Electrical Insulation. Vol.28, No.6, pp.984-1001 Dec. 1993 https://doi.org/10.1109/14.249372