Partial discharge signal extracting using the empirical mode decomposition with wavelet transform

Mei Yan Lin; Cheng Chi Tai; Ya Wen Tang; Ching Chau Su

doi:10.1109/APL.2011.6110158

Partial discharge signal extracting using the empirical mode decomposition with wavelet transform

Mei Yan Lin, Cheng Chi Tai, Ya Wen Tang, Ching Chau Su

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

22 Citations (Scopus)

Abstract

Empirical mode decomposition (EMD) has good adaptivity for non-stationary and nonlinear signal analysis. This paper uses the advantage of EMD and combines with the wavelet transform (EMD-WT) to extract partial discharge (PD) signals in noises. The wavelet transform is a common used method for PD signal denoising. However, once the signal to noise ratio (SNR) decreases seriously, the WT method will be failed. Compare to the WT method, the EMD-WT has better performance for noise reduction. It has been verified that the EMD-WT method can preserve more information even though the SNR is low. The results show that the EMD-WT is suitable for PD denoising in a noisy environment.

Original language	English
Title of host publication	2011 7th Asia-Pacific International Conference on Lightning, APL2011
Pages	420-424
Number of pages	5
DOIs	https://doi.org/10.1109/APL.2011.6110158
Publication status	Published - 2011 Dec 1
Event	2011 7th Asia-Pacific International Conference on Lightning, APL2011 - Chengdu, China Duration: 2011 Nov 1 → 2011 Nov 4

Publication series

Name	2011 7th Asia-Pacific International Conference on Lightning, APL2011

Other

Other	2011 7th Asia-Pacific International Conference on Lightning, APL2011
Country/Territory	China
City	Chengdu
Period	11-11-01 → 11-11-04

All Science Journal Classification (ASJC) codes

Environmental Chemistry

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10.1109/APL.2011.6110158

Cite this

Lin, M. Y., Tai, C. C., Tang, Y. W., & Su, C. C. (2011). Partial discharge signal extracting using the empirical mode decomposition with wavelet transform. In 2011 7th Asia-Pacific International Conference on Lightning, APL2011 (pp. 420-424). Article 6110158 (2011 7th Asia-Pacific International Conference on Lightning, APL2011). https://doi.org/10.1109/APL.2011.6110158

@inproceedings{eecedf9aae8e4239a7b3b7d1ad197692,

title = "Partial discharge signal extracting using the empirical mode decomposition with wavelet transform",

abstract = "Empirical mode decomposition (EMD) has good adaptivity for non-stationary and nonlinear signal analysis. This paper uses the advantage of EMD and combines with the wavelet transform (EMD-WT) to extract partial discharge (PD) signals in noises. The wavelet transform is a common used method for PD signal denoising. However, once the signal to noise ratio (SNR) decreases seriously, the WT method will be failed. Compare to the WT method, the EMD-WT has better performance for noise reduction. It has been verified that the EMD-WT method can preserve more information even though the SNR is low. The results show that the EMD-WT is suitable for PD denoising in a noisy environment.",

author = "Lin, {Mei Yan} and Tai, {Cheng Chi} and Tang, {Ya Wen} and Su, {Ching Chau}",

year = "2011",

month = dec,

day = "1",

doi = "10.1109/APL.2011.6110158",

language = "English",

isbn = "9781457714665",

series = "2011 7th Asia-Pacific International Conference on Lightning, APL2011",

pages = "420--424",

booktitle = "2011 7th Asia-Pacific International Conference on Lightning, APL2011",

note = "2011 7th Asia-Pacific International Conference on Lightning, APL2011 ; Conference date: 01-11-2011 Through 04-11-2011",

}

Lin, MY, Tai, CC, Tang, YW & Su, CC 2011, Partial discharge signal extracting using the empirical mode decomposition with wavelet transform. in 2011 7th Asia-Pacific International Conference on Lightning, APL2011., 6110158, 2011 7th Asia-Pacific International Conference on Lightning, APL2011, pp. 420-424, 2011 7th Asia-Pacific International Conference on Lightning, APL2011, Chengdu, China, 11-11-01. https://doi.org/10.1109/APL.2011.6110158

Partial discharge signal extracting using the empirical mode decomposition with wavelet transform. / Lin, Mei Yan; Tai, Cheng Chi; Tang, Ya Wen et al.
2011 7th Asia-Pacific International Conference on Lightning, APL2011. 2011. p. 420-424 6110158 (2011 7th Asia-Pacific International Conference on Lightning, APL2011).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Partial discharge signal extracting using the empirical mode decomposition with wavelet transform

AU - Lin, Mei Yan

AU - Tai, Cheng Chi

AU - Tang, Ya Wen

AU - Su, Ching Chau

PY - 2011/12/1

Y1 - 2011/12/1

N2 - Empirical mode decomposition (EMD) has good adaptivity for non-stationary and nonlinear signal analysis. This paper uses the advantage of EMD and combines with the wavelet transform (EMD-WT) to extract partial discharge (PD) signals in noises. The wavelet transform is a common used method for PD signal denoising. However, once the signal to noise ratio (SNR) decreases seriously, the WT method will be failed. Compare to the WT method, the EMD-WT has better performance for noise reduction. It has been verified that the EMD-WT method can preserve more information even though the SNR is low. The results show that the EMD-WT is suitable for PD denoising in a noisy environment.

AB - Empirical mode decomposition (EMD) has good adaptivity for non-stationary and nonlinear signal analysis. This paper uses the advantage of EMD and combines with the wavelet transform (EMD-WT) to extract partial discharge (PD) signals in noises. The wavelet transform is a common used method for PD signal denoising. However, once the signal to noise ratio (SNR) decreases seriously, the WT method will be failed. Compare to the WT method, the EMD-WT has better performance for noise reduction. It has been verified that the EMD-WT method can preserve more information even though the SNR is low. The results show that the EMD-WT is suitable for PD denoising in a noisy environment.

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T3 - 2011 7th Asia-Pacific International Conference on Lightning, APL2011

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Partial discharge signal extracting using the empirical mode decomposition with wavelet transform

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