Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding

Chan, Jeffery C., Ma, Hui, Saha, Tapan K. and Ekanayake, Chandima (2014) Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding. IEEE Transactions On Dielectrics and Electrical Insulation, 21 1: 294-303. doi:10.1109/TDEI.2014.6740752

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Author Chan, Jeffery C.
Ma, Hui
Saha, Tapan K.
Ekanayake, Chandima
Title Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding
Journal name IEEE Transactions On Dielectrics and Electrical Insulation   Check publisher's open access policy
ISSN 1070-9878
1558-4135
Publication date 2014-02-01
Year available 2014
Sub-type Article (original research)
DOI 10.1109/TDEI.2014.6740752
Open Access Status File (Author Post-print)
Volume 21
Issue 1
Start page 294
End page 303
Total pages 10
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Abstract This paper proposes a self-adaptive technique for partial discharge (PD) signal denoising with automatic threshold determination based on ensemble empirical mode decomposition (EEMD) and mathematical morphology. By introducing extra noise in the decomposition process, EEMD can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Through the kurtosis-based selection criterion, the IMFs embedded with PD impulses can be extracted for reconstruction. On the basis of mathematical morphology, an automatic morphological thresholding (AMT) technique is developed to form upper and lower thresholds for automatically eliminating the residual noise while maintaining the PD signals. The results on both simulated and real PD signals show that the above PD denoising technique is superior to wavelet transform (WT) and conventional EMD-based PD de-noising techniques.
Keyword De-noising
Ensemble empirical mode decomposition (EEMD)
High voltage (HV) equipment
Mathematical morphology
Partial discharge (PD)
Wavelet transform (WT)
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 19 times in Thomson Reuters Web of Science Article | Citations
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