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Discrimination of Air PD Sources Using Time-Frequency Distributions of PD Pulse Waveform  

Lee Kang-Won (한국철도기술연구원)
Kang Seong-Hwa (충청대 소방안전과)
Lim Ki-Joe (충북대 공대 전기전자공학부)
Publication Information
The Transactions of the Korean Institute of Electrical Engineers C / v.54, no.7, 2005 , pp. 332-338 More about this Journal
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
PD; STFT; Wavelet 2D; ANN;
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  • Reference
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