Open AccessSpectral features for the classification of partial discharge signals from selected insulation defect models
Author(s) -
Ambikairajah Raji,
Phung Bao Toan,
Ravishankar Jayashri,
Blackburn Trevor
Publication year - 2013
Publication title -
iet science, measurement and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 49
eISSN - 1751-8830
pISSN - 1751-8822
DOI - 10.1049/iet-smt.2012.0024
Subject(s) - pattern recognition (psychology) , frequency domain , octave (electronics) , wavelet , artificial intelligence , partial discharge , wavelet transform , classifier (uml) , speech recognition , fourier transform , computer science , mel frequency cepstrum , spectral density estimation , filter bank , filter (signal processing) , feature vector , feature extraction , mathematics , acoustics , physics , computer vision , voltage , mathematical analysis , quantum mechanics
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.