Open AccessDenoising different types of acoustic partial discharge signals using power spectral subtraction
Author(s) -
Hussein Ramy,
Shaban Khaled Bashir,
ElHag Ayman H.
Publication year - 2018
Publication title -
high voltage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.732
H-Index - 20
ISSN - 2397-7264
DOI - 10.1049/hve.2017.0119
Subject(s) - noise reduction , white noise , noise measurement , noise (video) , acoustics , computer science , partial discharge , noise spectral density , wavelet , additive white gaussian noise , speech recognition , mathematics , artificial intelligence , engineering , physics , telecommunications , electrical engineering , noise figure , bandwidth (computing) , voltage , amplifier , image (mathematics)
Measuring acoustic emission (AE) of partial discharge (PD) phenomena can be adopted to estimate the condition of power transformers. However, the environmental noise encountered with AE of PD measurements negatively affects the accuracy of PD localisation and classification. Thus, efficient signal denoising techniques are required for noise suppression and hence, better detection accuracy. This study deals with white noise and it is a continuation of a previously published work that deals with random noise. The published work addresses the random noise suppression using a method named, power spectral subtraction denoising (PSSD). This study applies PSSD to the PD signals contaminated with white noise and uses a novel scheme of noise power spectrum density estimation. Multiple types of AE signals are examined including signals produced by corona, surface, parallel, and void PDs. Synthetic and real data demonstrate the superiority of the proposed method over the wavelet shrinkage denoising method as it can more effectively eliminate white noise and preserve signals of low signal‐to‐noise ratio.