Power system transformerboard degradation detection using probabilistic neural network
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Date
2012
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Asian Research Publishing Network (ARPN)
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
The insulation condition monitoring of a power transformer has an important role for insulating materials which are subjected to extensive breakdown stress. In this study, a test setup has been constructed in order to simulate real world breakdown characteristics of transformerboards which are widely used as the insulating material. During the service life transformerboards may display undesired surface discharge damage due to increased rated voltages, which reduces the lifetime of transformerboards. The probabilistic neural network is used to detect the surface degradation of a transformerboard by analyzing electrical and ultrasound discharge data obtained from the test setup. The principle component analysis is employed to eliminate the messy matrix and vector calculations of the probabilistic neural network operations. Results of the classification procedure are given. © 2005 - 2012 JATIT & LLS.
Description
Keywords
Principle component analysis, Probabilistic neural network, Transformerboard
Journal or Series
Journal of Theoretical and Applied Information Technology
WoS Q Value
Scopus Q Value
Q4
Volume
42
Issue
1
