DocumentCode :
2485695
Title :
Prediction of polluted insulators characteristics using artificial neural networks
Author :
Teguar, M. ; Mekhaldi, A. ; Boubakeur, A.
Author_Institution :
Dept. d´´Electrotech., Ecole Nat. Polytech., Algiers, Algeria
fYear :
2012
fDate :
14-17 Oct. 2012
Firstpage :
767
Lastpage :
770
Abstract :
In this paper, we propose three prediction algorithms using the artificial neural networks to generalise some characteristics describing the electrical arc propagation on polluted insulators. For that purpose, three Radial Basis Function Gaussian (RBFG) networks with one output have been elaborated. The difference between these configurations consists in the nature of the input and output units. The chosen networks are trained by Random Optimisation Method (ROM). A discussion to determine the best configuration is presented.
Keywords :
arcs (electric); insulators; optimisation; power engineering computing; radial basis function networks; RBFG networks; ROM; artificial neural networks; electrical arc propagation; polluted insulator characteristic prediction algorithm; radial basis function Gaussian networks; random optimisation method; Artificial neural networks; Conductivity; Electrodes; Insulators; Leakage current; Pollution; Read only memory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Insulation and Dielectric Phenomena (CEIDP), 2012 Annual Report Conference on
Conference_Location :
Montreal, QC
ISSN :
0084-9162
Print_ISBN :
978-1-4673-1253-0
Electronic_ISBN :
0084-9162
Type :
conf
DOI :
10.1109/CEIDP.2012.6378893
Filename :
6378893
Link To Document :
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