Design of an artificial neural network for the estimation of the flashover voltage on insulators

Kontargyri, VT; Gialketsi, AA; Tsekouras, GJ; Gonos, IF; Stathopulos, IA

dc.contributor.author	Kontargyri, VT	en
dc.contributor.author	Gialketsi, AA	en
dc.contributor.author	Tsekouras, GJ	en
dc.contributor.author	Gonos, IF	en
dc.contributor.author	Stathopulos, IA	en
dc.date.accessioned	2014-03-01T01:26:05Z
dc.date.available	2014-03-01T01:26:05Z
dc.date.issued	2007	en
dc.identifier.issn	0378-7796	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17920
dc.subject	Artificial neural network	en
dc.subject	Critical flashover voltage	en
dc.subject	High voltage insulators	en
dc.subject	Polluted insulators	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Density (specific gravity)	en
dc.subject.other	Electrodeposition	en
dc.subject.other	Flashover	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Critical flashover voltage	en
dc.subject.other	High voltage insulators	en
dc.subject.other	Polluted insulators	en
dc.subject.other	Electric insulators	en
dc.title	Design of an artificial neural network for the estimation of the flashover voltage on insulators	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.epsr.2006.10.017	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.epsr.2006.10.017	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	This work attempts to apply an artificial neural network in order to estimate the critical flashover voltage on polluted insulators. The artificial neural network uses as input variables the following characteristics of the insulator: diameter, height, creepage distance, form factor and equivalent salt deposit density. and estimates the critical flashover voltage. The data used to train the network and test its performance is derived from experimental measurements and a mathematical model. Various cases have been studied and their results presented separately. Training and testing sets have been modified for each case. (C) 2006 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Electric Power Systems Research	en
dc.identifier.doi	10.1016/j.epsr.2006.10.017	en
dc.identifier.isi	ISI:000250134200002	en
dc.identifier.volume	77	en
dc.identifier.issue	12	en
dc.identifier.spage	1532	en
dc.identifier.epage	1540	en