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Wavelet and mathematical morphology as the de-noising methods for PD analysis of high voltage transformer windings

Wavelet and mathematical morphology as the de-noising methods for PD analysis of high voltage transformer windings
Wavelet and mathematical morphology as the de-noising methods for PD analysis of high voltage transformer windings
Partial discharge (PD) analysis is one of the most important techniques to evaluate the condition of the insulation systems within high voltage (HV) transformers. However, in typical field environments, measurements of PD signals can be distorted by noise sources. This greatly reduces the ability to identify PD sources in HV transformer windings. Therefore, denoising methods in PD analysis are very important. In recent years, several noise reduction techniques have been proposed for application in PD analysis. The common types of discharge events that may occur within high voltage transformer windings namely void, surface, corona and floating discharge have been experimentally generated. Each type of discharge was injected into different locations along a HV transformer winding and then measured using two wideband radio frequency current transformers (RFCTs) positioned at each end of the winding. Then, either the Discrete Wavelet Transform (DWT) and or Mathematical Stationary Wavelet Transform (SWT) or Mathematical Morphology (MM) were applied to reduce the noise in the raw captured PD signals. This paper presents the comparison of performance of the techniques in terms of noise reduction for this type of application.
partial discharge, de-noising, transformer windings
978-1-4799-7354-5
214-217
Nik Ali, N.H.
91f9aa04-0cd9-4d62-896f-97584753886d
Abd Rahman, M.S.
8217f865-63cf-4fa4-9f2b-19589bbdb2f0
Hunter, J.A.
dae3e13b-a97e-4e81-a617-20ab6965da3c
Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Rapisarda, P.
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b
Nik Ali, N.H.
91f9aa04-0cd9-4d62-896f-97584753886d
Abd Rahman, M.S.
8217f865-63cf-4fa4-9f2b-19589bbdb2f0
Hunter, J.A.
dae3e13b-a97e-4e81-a617-20ab6965da3c
Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Rapisarda, P.
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b

Nik Ali, N.H., Abd Rahman, M.S., Hunter, J.A., Lewin, P.L. and Rapisarda, P. (2015) Wavelet and mathematical morphology as the de-noising methods for PD analysis of high voltage transformer windings. 2015 IEEE Electrical Insulation Conference (EIC), United States. 07 - 10 Jun 2015. pp. 214-217 .

Record type: Conference or Workshop Item (Paper)

Abstract

Partial discharge (PD) analysis is one of the most important techniques to evaluate the condition of the insulation systems within high voltage (HV) transformers. However, in typical field environments, measurements of PD signals can be distorted by noise sources. This greatly reduces the ability to identify PD sources in HV transformer windings. Therefore, denoising methods in PD analysis are very important. In recent years, several noise reduction techniques have been proposed for application in PD analysis. The common types of discharge events that may occur within high voltage transformer windings namely void, surface, corona and floating discharge have been experimentally generated. Each type of discharge was injected into different locations along a HV transformer winding and then measured using two wideband radio frequency current transformers (RFCTs) positioned at each end of the winding. Then, either the Discrete Wavelet Transform (DWT) and or Mathematical Stationary Wavelet Transform (SWT) or Mathematical Morphology (MM) were applied to reduce the noise in the raw captured PD signals. This paper presents the comparison of performance of the techniques in terms of noise reduction for this type of application.

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More information

Published date: 7 June 2015
Venue - Dates: 2015 IEEE Electrical Insulation Conference (EIC), United States, 2015-06-07 - 2015-06-10
Keywords: partial discharge, de-noising, transformer windings
Organisations: EEE

Identifiers

Local EPrints ID: 378066
URI: http://eprints.soton.ac.uk/id/eprint/378066
ISBN: 978-1-4799-7354-5
PURE UUID: 07cf64ab-8bd3-4d0e-b335-8806b5375bdd

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Date deposited: 15 Jun 2015 17:14
Last modified: 16 Oct 2018 16:31

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Contributors

Author: N.H. Nik Ali
Author: M.S. Abd Rahman
Author: J.A. Hunter
Author: P.L. Lewin
Author: P. Rapisarda

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