A novel wavelet selection scheme for partial discharge signal detection under low SNR condition

Jiajia Liu, W.H. Siew, J.J. Soraghan, Xiao Hu, Xiaosheng Peng, Euan A. Morris

Research output: Contribution to journalArticle

Abstract

Wavelet-based techniques have been widely used to extract partial discharge (PD) signals from noisy signals. Generally, the procedure consists of 3 steps: wavelet selection, decomposition scale determination, and noise estimation. Wavelet selection is the first and most important step for its successful application in PD denoising. However, despite many variants of techniques deployed, the success rate is not generally good especially when the signal to noise ratio is unity or less. This paper discusses a novel technique that addresses this issue. The technique is inspired by the concept of Shannon entropy and the associated information cost functions (ICF) in information theory. It is adaptive to the detected PD signals. The paper demonstrates that the proposed technique is effective when applied to PD signals obtained through laboratory experiments and on-site measurements. When this technique is applied to cable diagnostics, it should have the potential to extend the range of PD detection from cables.
LanguageEnglish
Number of pages19
JournalCIGRE Science and Engineering
Volume14
Publication statusPublished - 30 Jun 2019

Fingerprint

Partial discharges
Signal detection
Cables
Information theory
Cost functions
Signal to noise ratio
Entropy
Decomposition
Experiments

Keywords

  • denoising
  • detection
  • partial dishcarge
  • wavelet selection
  • wavelet entropy

Cite this

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title = "A novel wavelet selection scheme for partial discharge signal detection under low SNR condition",
abstract = "Wavelet-based techniques have been widely used to extract partial discharge (PD) signals from noisy signals. Generally, the procedure consists of 3 steps: wavelet selection, decomposition scale determination, and noise estimation. Wavelet selection is the first and most important step for its successful application in PD denoising. However, despite many variants of techniques deployed, the success rate is not generally good especially when the signal to noise ratio is unity or less. This paper discusses a novel technique that addresses this issue. The technique is inspired by the concept of Shannon entropy and the associated information cost functions (ICF) in information theory. It is adaptive to the detected PD signals. The paper demonstrates that the proposed technique is effective when applied to PD signals obtained through laboratory experiments and on-site measurements. When this technique is applied to cable diagnostics, it should have the potential to extend the range of PD detection from cables.",
keywords = "denoising, detection, partial dishcarge, wavelet selection, wavelet entropy",
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AU - Liu, Jiajia

AU - Siew, W.H.

AU - Soraghan, J.J.

AU - Hu, Xiao

AU - Peng, Xiaosheng

AU - Morris, Euan A.

PY - 2019/6/30

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AB - Wavelet-based techniques have been widely used to extract partial discharge (PD) signals from noisy signals. Generally, the procedure consists of 3 steps: wavelet selection, decomposition scale determination, and noise estimation. Wavelet selection is the first and most important step for its successful application in PD denoising. However, despite many variants of techniques deployed, the success rate is not generally good especially when the signal to noise ratio is unity or less. This paper discusses a novel technique that addresses this issue. The technique is inspired by the concept of Shannon entropy and the associated information cost functions (ICF) in information theory. It is adaptive to the detected PD signals. The paper demonstrates that the proposed technique is effective when applied to PD signals obtained through laboratory experiments and on-site measurements. When this technique is applied to cable diagnostics, it should have the potential to extend the range of PD detection from cables.

KW - denoising

KW - detection

KW - partial dishcarge

KW - wavelet selection

KW - wavelet entropy

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M3 - Article

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JO - CIGRE Science and Engineering

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