A novel wavelet selection scheme for partial discharge signal denoising

Research output: Contribution to conferencePoster

Abstract

Over the past two decades, wavelet-based techniques have been widely used to extract partial discharge (PD) signals from noisy signals. To effectively select the correct technique to minimize the effect of noise on PD detection, three aspects are considered: wavelet selection, decomposition scale, and noise or threshold estimation. For wavelet selection, popular techniques, including correlation-based wavelet selection scheme (CBWSS) and energy-based wavelet selection scheme (EBWSS), are applied to select an appropriate wavelet basis function. These two schemes, however, have their limitations. CBWSS is not as effective as expected when the signal to noise ratio (SNR) is very low. EBWSS selects the optimal wavelet that can maximize the energy ratio of the PD signal in approximation coefficients through wavelet decomposition. It is not strictly true for damped oscillating PD signals, particularly when the decomposition scale increases. As such, a novel wavelet selection scheme, wavelet entropy-based wavelet selection scheme ( WEBWSS), is proposed to provide an alternative to CBWSS and EBWSS for PD denoising. PD signals are simulated and also obtained through laboratory experiments to demonstrate that this new method has better performance in the removal of noise, particularly when SNR is low.
LanguageEnglish
Pages1-4
Number of pages4
Publication statusPublished - 29 Nov 2018
EventConference on Electrical Insulation and Dielectric Phenomena - Cancun, Cancun, Mexico
Duration: 21 Oct 201824 Oct 2018

Conference

ConferenceConference on Electrical Insulation and Dielectric Phenomena
CountryMexico
CityCancun
Period21/10/1824/10/18

Fingerprint

Signal denoising
Partial discharges
Signal to noise ratio
Wavelet decomposition
Entropy

Keywords

  • wavelet-based technique
  • partial discharge
  • detection
  • denoising
  • wavelet selection
  • SNR
  • wavelet entropy

Cite this

Liu, J., Siew, W. H., Soraghan, J. J., & Morris, E. A. (2018). A novel wavelet selection scheme for partial discharge signal denoising. 1-4. Poster session presented at Conference on Electrical Insulation and Dielectric Phenomena, Cancun, Mexico.
Liu, Jiajia ; Siew, W.H. ; Soraghan, John J. ; Morris, Euan A. / A novel wavelet selection scheme for partial discharge signal denoising. Poster session presented at Conference on Electrical Insulation and Dielectric Phenomena, Cancun, Mexico.4 p.
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title = "A novel wavelet selection scheme for partial discharge signal denoising",
abstract = "Over the past two decades, wavelet-based techniques have been widely used to extract partial discharge (PD) signals from noisy signals. To effectively select the correct technique to minimize the effect of noise on PD detection, three aspects are considered: wavelet selection, decomposition scale, and noise or threshold estimation. For wavelet selection, popular techniques, including correlation-based wavelet selection scheme (CBWSS) and energy-based wavelet selection scheme (EBWSS), are applied to select an appropriate wavelet basis function. These two schemes, however, have their limitations. CBWSS is not as effective as expected when the signal to noise ratio (SNR) is very low. EBWSS selects the optimal wavelet that can maximize the energy ratio of the PD signal in approximation coefficients through wavelet decomposition. It is not strictly true for damped oscillating PD signals, particularly when the decomposition scale increases. As such, a novel wavelet selection scheme, wavelet entropy-based wavelet selection scheme ( WEBWSS), is proposed to provide an alternative to CBWSS and EBWSS for PD denoising. PD signals are simulated and also obtained through laboratory experiments to demonstrate that this new method has better performance in the removal of noise, particularly when SNR is low.",
keywords = "wavelet-based technique, partial discharge, detection, denoising, wavelet selection, SNR, wavelet entropy",
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note = "{\circledC} 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; Conference on Electrical Insulation and Dielectric Phenomena ; Conference date: 21-10-2018 Through 24-10-2018",
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Liu, J, Siew, WH, Soraghan, JJ & Morris, EA 2018, 'A novel wavelet selection scheme for partial discharge signal denoising' Conference on Electrical Insulation and Dielectric Phenomena, Cancun, Mexico, 21/10/18 - 24/10/18, pp. 1-4.

A novel wavelet selection scheme for partial discharge signal denoising. / Liu, Jiajia; Siew, W.H.; Soraghan, John J.; Morris, Euan A.

2018. 1-4 Poster session presented at Conference on Electrical Insulation and Dielectric Phenomena, Cancun, Mexico.

Research output: Contribution to conferencePoster

TY - CONF

T1 - A novel wavelet selection scheme for partial discharge signal denoising

AU - Liu, Jiajia

AU - Siew, W.H.

AU - Soraghan, John J.

AU - Morris, Euan A.

N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2018/11/29

Y1 - 2018/11/29

N2 - Over the past two decades, wavelet-based techniques have been widely used to extract partial discharge (PD) signals from noisy signals. To effectively select the correct technique to minimize the effect of noise on PD detection, three aspects are considered: wavelet selection, decomposition scale, and noise or threshold estimation. For wavelet selection, popular techniques, including correlation-based wavelet selection scheme (CBWSS) and energy-based wavelet selection scheme (EBWSS), are applied to select an appropriate wavelet basis function. These two schemes, however, have their limitations. CBWSS is not as effective as expected when the signal to noise ratio (SNR) is very low. EBWSS selects the optimal wavelet that can maximize the energy ratio of the PD signal in approximation coefficients through wavelet decomposition. It is not strictly true for damped oscillating PD signals, particularly when the decomposition scale increases. As such, a novel wavelet selection scheme, wavelet entropy-based wavelet selection scheme ( WEBWSS), is proposed to provide an alternative to CBWSS and EBWSS for PD denoising. PD signals are simulated and also obtained through laboratory experiments to demonstrate that this new method has better performance in the removal of noise, particularly when SNR is low.

AB - Over the past two decades, wavelet-based techniques have been widely used to extract partial discharge (PD) signals from noisy signals. To effectively select the correct technique to minimize the effect of noise on PD detection, three aspects are considered: wavelet selection, decomposition scale, and noise or threshold estimation. For wavelet selection, popular techniques, including correlation-based wavelet selection scheme (CBWSS) and energy-based wavelet selection scheme (EBWSS), are applied to select an appropriate wavelet basis function. These two schemes, however, have their limitations. CBWSS is not as effective as expected when the signal to noise ratio (SNR) is very low. EBWSS selects the optimal wavelet that can maximize the energy ratio of the PD signal in approximation coefficients through wavelet decomposition. It is not strictly true for damped oscillating PD signals, particularly when the decomposition scale increases. As such, a novel wavelet selection scheme, wavelet entropy-based wavelet selection scheme ( WEBWSS), is proposed to provide an alternative to CBWSS and EBWSS for PD denoising. PD signals are simulated and also obtained through laboratory experiments to demonstrate that this new method has better performance in the removal of noise, particularly when SNR is low.

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KW - partial discharge

KW - detection

KW - denoising

KW - wavelet selection

KW - SNR

KW - wavelet entropy

UR - https://ieeedeis.org/ceidp2/

M3 - Poster

SP - 1

EP - 4

ER -

Liu J, Siew WH, Soraghan JJ, Morris EA. A novel wavelet selection scheme for partial discharge signal denoising. 2018. Poster session presented at Conference on Electrical Insulation and Dielectric Phenomena, Cancun, Mexico.