NDLI: Using counterpropagation neural networks for partial discharge diagnosis

Content Provider	Springer Nature Link
Author	Freisleben, B. Hoof, M. Patsch, R.
Copyright Year	1998
Abstract	In high voltage engineering, various methods of non-destructive fault diagnosis are applied for investigating the quality of insulating materials and systems. The methods are aimed at classifying patterns derived from the measured characteristics of the electrical signals typically resulting from insulation defects. In this paper, variants of the counterpropagation neural network architecture are used to classify patterns representing various properties of partial discharges. It is shown that the classification quality can be improved considerably when an extended counterpropagation network with a dynamically changing network topology, and an additional vigilance unit for monitoring the behaviour of the network during the learning phase is applied. The extended network has significant advantages over the standard counterpropagation network in cases where outliers in the training data seriously degrade the approximation quality of the standard network. When using the proposed network in conjunction with physically motivated discharge data, input patterns from defect categories not considered during training can be rejected more reliably. This rejection problem is particularly important for practical applications where misclassifications cannot be tolerated.
Starting Page	318
Ending Page	333
Page Count	16
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	7
Issue Number	4
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	1998-01-01
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Counterpropagation High voltage engineering Non-destructive testing Partial discharge diagnosis Pattern classification Vigilant counterpropagation Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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