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Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition.

Authors:
Ragavesh Dhandapani Imene Mitiche Scott McMeekin Venkateswara Sarma Mallela Gordon Morison

Entropy (Basel) 2021 Nov 25;23(12). Epub 2021 Nov 25.

Department of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK.

This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length . Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into number of Band Limited IMFs (BLIMFs). The BLIMFs are separated as noise, noise-dominant, and signal-dominant BLIMFs by calculating the MI between BLIMFs. Eventually, the noise BLIMFs are discarded from further processing, noise-dominant BLIMFs are denoised using GSTV, and the signal BLIMFs are added to reconstruct the output signal. The regularization parameter λ for GSTV is automatically selected based on the values of Dispersion Entropy of the noise-dominant BLIMFs. The effectiveness of the proposed denoising method is evaluated in terms of performance metrics such as Signal-to-Noise Ratio, Root Mean Square Error, and Correlation Coefficient, which are are compared to EMD variants, and the results demonstrated that the proposed approach is able to effectively denoise the synthetic Blocks, Bumps, Doppler, Heavy Sine, PD pulses and real PD signals.

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http://dx.doi.org/10.3390/e23121567DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700104PMC
November 2021

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