Partial discharge signal self-adaptive sparse decomposition noise abatement based on spectral kurtosis and S-transform
ISSN: 0332-1649
Article publication date: 2 January 2018
Abstract
Purpose
Noise abatement is one of the key techniques for Partial Discharge (PD) on-line measurement and monitoring. However, how to enhance the efficiency of PD signal noise suppression is a challenging work. Hence, this study aims to improve the efficiency of PD signal noise abatement.
Design/methodology/approach
In this approach, the time–frequency characteristics of PD signal had been obtained based on fast kurtogram and S-transform time–frequency spectrum, and these characteristics were used to optimize the parameters for the signal matching over-complete dictionary. Subsequently, a self-adaptive selection of matching atoms was realized when using Matching Pursuit (MP) to analyze PD signals, which leading to seldom noise signal element was represented in sparse decomposition.
Findings
The de-noising of PD signals was achieved efficiently. Simulation and experimental results show that the proposed method has good adaptability and significant noise abatement effect compared with Empirical Mode Decomposition, Wavelet Threshold and global signal sparse decomposition of MP.
Originality/value
A self-adaptive noise abatement method was proposed to improve the efficiency of PD signal noise suppression based on the signal sparse representation and its MP algorithm, which is significant to on-line PD measurement.
Keywords
Citation
Zhang, A., He, C., Sun, M., Li, Q., Li, H.W. and Yang, L. (2018), "Partial discharge signal self-adaptive sparse decomposition noise abatement based on spectral kurtosis and S-transform", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 37 No. 1, pp. 293-306. https://doi.org/10.1108/COMPEL-03-2017-0126
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited