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AuthorHarbaji, Mustafa
AuthorEl-Hag, Ayman
AuthorShaban, Khaled
Available date2022-12-21T10:01:45Z
Publication Date2013
Publication Name2013 3rd International Conference on Electric Power and Energy Conversion Systems, EPECS 2013
ResourceScopus
URIhttp://dx.doi.org/10.1109/EPECS.2013.6713000
URIhttp://hdl.handle.net/10576/37488
AbstractAccurate partial discharge (PD) classification provides significant information to asses power transformers' insulation condition. The work presented in this paper aims to improve classification from acoustic emission signals for oil-paper insulated systems. Three different types of PDs are considered; surface discharge, PD from a sharp point to ground electrode, and PD from parallel plates. The PD types are simulated with aged insulation material (oil/paper), large tank size, and high surrounding noise level. The signals collected from each PD type are preprocessed using Continuous Wavelet Transform. The preprocessed signals are compressed using zonal coding applied over Direct Cosine Transform coefficients to create the feature vectors for classification, where a feed-forward with back-propagation trained neural network is utilized. The results indicates high recognition rate for classifying the different PD types using the proposed method. 2013 IEEE.
SponsorQatar National Research Fund
Languageen
SubjectAcoustic Emission Signals
Continuous Wavelet Transform
Direct Cosine Transform
Partial Discharge Classification
Zonal Coding
TitleAccurate partial discharge classification from acoustic emission signals
TypeConference Paper
dc.accessType Abstract Only


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