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A New Feature Space for Partial Discharge Signal Separation Based on DWT Coefficient Variance



Author(s)

Maillot Marcos Uriel and Pessana Franco Martín

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DOI:10.17265/2328-2223/2018.01.003

In this paper, a new feature space for PD (partial discharge) signal separation is presented. Three typical PD defects were experimentally reproduced in a laboratory for obtaining independent PD sources. Signals were acquired with a digital storage oscilloscope and then post-processed with DWT (discrete Wavelet transform) for de-noising. The new feature space for PD source separation was constructed with the variance of each Wavelet coefficient vector and was compared with an established feature space for PD source separation; based on the energy of DWT coefficient vectors. After a space reduction by mean of PCA (principal components analysis), the separation capability among them was measured by comparing the final classification error after training a neural network. Results showed that with this new feature space it is possible to separate different sources of PD signals. Later, the feature space proposed was used to separate two PD sources from a real equipment tested. Further analysis on the reduced feature space has shown the band location of PD signals information for separating purpose.

Partial discharges, wavelet transform, singular value decomposition, feature extraction, signal processing.