Artificial neural network optimisation methodology for the estimation of the critical flashover voltage on insulators

Asimakopoulou, GE; Kontargyri, VT; Tsekouras, GJ; Asimakopoulou, FE; Gonos, IF; Stathopulos, IA

dc.contributor.author	Asimakopoulou, GE	en
dc.contributor.author	Kontargyri, VT	en
dc.contributor.author	Tsekouras, GJ	en
dc.contributor.author	Asimakopoulou, FE	en
dc.contributor.author	Gonos, IF	en
dc.contributor.author	Stathopulos, IA	en
dc.date.accessioned	2014-03-01T01:29:52Z
dc.date.available	2014-03-01T01:29:52Z
dc.date.issued	2009	en
dc.identifier.issn	1751-8822	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19390
dc.subject	Artificial Neural Network	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Approximation theory	en
dc.subject.other	Backpropagation	en
dc.subject.other	Neural networks	en
dc.subject.other	ANN trainings	en
dc.subject.other	Artificial neural networks	en
dc.subject.other	Confidence intervals	en
dc.subject.other	Correlation indices	en
dc.subject.other	Critical flashover voltages	en
dc.subject.other	Crucial parameters	en
dc.subject.other	Equivalent salt deposit densities	en
dc.subject.other	Form factors	en
dc.subject.other	Input variables	en
dc.subject.other	Optimisation	en
dc.subject.other	Polluted insulators	en
dc.subject.other	Re samplings	en
dc.subject.other	Test sets	en
dc.subject.other	Training sets	en
dc.subject.other	Flashover	en
dc.title	Artificial neural network optimisation methodology for the estimation of the critical flashover voltage on insulators	en
heal.type	journalArticle	en
heal.identifier.primary	10.1049/iet-smt:20080009	en
heal.identifier.secondary	http://dx.doi.org/10.1049/iet-smt:20080009	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	To describe an artificial neural network (ANN) methodology in order to estimate the critical flashover voltage on polluted insulators is the objective here. The methodology uses as input variables characteristics of the insulator such as diameter, height, creepage distance, form factor and equivalent salt deposit density, and it estimates the critical flashover voltage based on an ANN. For each ANN training algorithm, an optimisation process is conducted regarding the values of crucial parameters such as the number of neurons and so on using the training set. The success of each algorithm in estimating the critical flashover voltage is measured by the correlation index between the experimental and estimated values for the evaluation set, and finally the ANN with the correlation index closest to 1 is specified. For this ANN and the respective algorithm, the critical flashover voltage of the test set insulators is estimated and the respective confidence intervals are calculated through the re-sampling method. © 2008 The Institution of Engineering and Technology.	en
heal.publisher	INST ENGINEERING TECHNOLOGY-IET	en
heal.journalName	IET Science, Measurement and Technology	en
dc.identifier.doi	10.1049/iet-smt:20080009	en
dc.identifier.isi	ISI:000262874500011	en
dc.identifier.volume	3	en
dc.identifier.issue	1	en
dc.identifier.spage	90	en
dc.identifier.epage	104	en