Development of Hankel‐SVD hybrid technique for multiple noise removal from PD signature. Issue 8 (1st October 2019)

• Record Type: Journal Article
• Authors: Govindarajan, Suganya; Subbaiah, Jayalalitha; Cavallini, Andrea; Krithivasan, Kannan; Jayakumar, Jaikanth
• Is Part Of: IET science, measurement & technology. Volume 13:Issue 8(2019)
• Page Start: 1075
• Page End: 1084
• Publication Date: 2019-10-01
• Subjects: partial discharges -- signal denoising -- interference suppression -- partial discharge measurement -- filtering theory -- singular value decomposition -- white noise -- Hankel matrices; universal method -- interference removal -- measured pulse -- H‐SVD filter -- simulated PD data -- real‐time PD data -- synthetic noises -- Hankel‐SVD hybrid technique -- multiple noise removal -- PD signature -- partial discharge phenomena -- PD signals -- white noises -- suppress interferences -- signal space -- PD source separation -- insulation integrity assessment -- Hankel matrix‐based singular value decomposition filter -- Hankel matrix‐based singular value decomposition filter -- PD denoising methods; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2018.5679
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : Detection and measurement of partial discharge (PD) phenomena combined with the separation and identification of PD sources is the way to achieve effective insulation integrity assessment. However, during measurement, PD signals are coupled with interferences (discrete spectral, pulsive, and white noises). Recovering PD signals from such interferences would improve PD source separation (thus identification), but still remains a challenging task. Several denoising methods have been proposed to suppress interferences. However, using a universal method to achieve interference removal is probably impossible, as the characteristics of the interferences are distinct. This study proposes a novel low‐rank H‐Matrix‐based singular value decomposition (SVD) filter (H‐SVD) that removes different types of interferences. Denoising is done by projecting the measured pulse in a lower dimensional signal space. To assess the effectiveness of the proposed method, H‐SVD filter is first applied to simulated PD data and later on real‐time PD data with the introduction of three different types of synthetic noises. The results of the evaluation metrics confirm that H‐SVD has significant performance improvements compared to existing state‐of‐the‐art PD denoising methods.