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

Ragavesh Dhandapani,Imene Mitiche,Gordon Morison,Venkateswara Sarma Mallela,Scott Mcmeekin

doi:10.3390/e23121567

Abstract

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 K. Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into K 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 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.

Highlights

High Voltage (HV) equipment uses a variety of insulating materials for protecting the system and have dielectric media that are either solid, liquid, or gas depending on the design requirements of HV equipment
The Mutual Information (MI) value 2.3165 of I MF4−5 is above the threshold value 0.9 for Empirical Mode Decomposition (EMD) and 1.4764 of I MF1−2 is above the threshold value 0.6 for Variational Mode Decomposition (VMD), I MF5 and I MF2 are selected as the boundary between noise and signal-dominant Intrinsic Mode Functions (IMFs) in EMD and VMD methods, respectively
Our approach is to decompose the signal using VMD with the mode parameter set by number of EMD IMFs with MI analysis, later VMD IMFs are analyzed using MI to group noise-dominant and signaldominant IMFs

Summary

Introduction

High Voltage (HV) equipment uses a variety of insulating materials for protecting the system and have dielectric media that are either solid, liquid, or gas depending on the design requirements of HV equipment. Due to the high voltage stress, localized dielectric breakdown of a small portion of an insulator occurs, resulting in a Partial Discharge (PD) signal. The most common issue faced during on-line PD measurements is the interference of external signal referred as noise, which sometimes has a very high amplitude compared to the PD signal [2,3]. The most common on-site noise signals reported during PD measurements are noise from corona discharge, white Gaussian noise, thermal noise, pink noise, and high-frequency signal interference from communication equipment, commonly referred to as Discrete Spectral Interference (DSI) [4,5]

Methods

Results

Conclusion