Denoising different types of acoustic partial discharge signals using power spectral subtraction. Issue 1 (1st March 2018)

• Record Type: Journal Article
• Authors: Hussein, Ramy; Shaban, Khaled Bashir; El‐Hag, Ayman H.
• Is Part Of: High voltage. Volume 3:Issue 1(2018)
• Page Start: 44
• Page End: 50
• Publication Date: 2018-03-01
• Subjects: signal denoising -- partial discharge measurement -- acoustic emission -- power transformers -- corona; acoustic partial discharge signal denoising -- power spectral subtraction -- acoustic emission measurement -- partial discharge phenomena -- PD phenomena -- power transformers -- environmental noise -- PD measurements -- PD localisation -- PD classification -- signal denoising techniques -- noise suppression -- power spectral subtraction denoising -- random noise suppression -- PSSD -- noise power spectrum density estimation -- AE signals -- corona -- void PD -- wavelet shrinkage denoising method -- signal‐to‐noise ratio; High voltages -- Periodicals; 621.3191
• DOI: 10.1049/hve.2017.0119
• Languages: English
• ISSNs: 2397-7264

Abstract

Abstract : 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.