| Autor: |
Ambikairajah, Raji, Phung, Bao Toan, Ravishankar, Jayashri, Blackburn, Trevor |
| Zdroj: |
IET Science, Measurement & Technology (Wiley-Blackwell); Mar2013, Vol. 7 Issue 2, p104-111, 8p |
| Abstrakt: |
Time‐domain features of partial discharge (PD) signals are often used to classify PD patterns. This paper proposes spectral features that are extracted using a filter bank, consisting of band‐pass filters. By applying the fast Fourier transform to the PD signal, the resulting frequency bins are grouped into L octave frequency sub‐bands. Two new features called the octave frequency moment coefficients (OFMC) and octave frequency Cepstral coefficients (OFCC) are defined in this paper. In addition, time–frequency domain coefficients (TFDC) obtained via wavelet analysis are also analysed. A PD signal can now be represented as an L‐dimensional feature vector of OFMC, OFCC or TFDC. These features are compared with discrete wavelet transform‐based higher‐order statistical features (HOSF) using three different classifiers: probabilistic neural network, support vector machine and the recently emerged sparse representation classifier. Results show that the proposed spectral features are robust and provide a better classification accuracy of PD signals, compared with HOSF. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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