Comparison between artificial neural networks algorithms for the estimation of the flashover voltage on insulators

dc.contributor.author	Κονταργύρη, Βασιλική	el
dc.contributor.author	Κονταξής, Παναγιώτης	el
dc.contributor.author	Γιαλκέτση, Α.	el
dc.contributor.author	Τσεκούρας, Γιώργος	el
dc.date.accessioned	2015-05-25T17:24:19Z
dc.date.available	2015-05-25T17:24:19Z
dc.date.issued	2015-05-25
dc.identifier.uri	http://hdl.handle.net/11400/11136
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.wseas.org	en
dc.subject	Ενέργεια
dc.subject	High voltage insulators
dc.subject	Μονωτές υψηλής τάσης
dc.subject	Τεχνητά δίκτυα νεύρων
dc.subject	Αλγόριθμοι
dc.subject	Algorithms
dc.title	Comparison between artificial neural networks algorithms for the estimation of the flashover voltage on insulators	en
heal.type	conferenceItem
heal.classification	Technology
heal.classification	Electronics
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρονική
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85042383
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρονική
heal.language	en
heal.access	campus
heal.publicationDate	2008-05-02
heal.bibliographicCitation	Kontargyri, V., Tsekouras, G., Gialketsi, A. and Kontaxis, P. (2008) Comparison between artificial neural networks algorithms for the estimation of the flashover voltage on insulators. In 9th WSEAS International Conference on Neural Networks. 2nd to 4th May 2008. Sofia	en
heal.abstract	This work attempts to apply Artificial Neural Networks in order to estimate the critical flashover voltage on polluted insulators. First, an ANN was constructed in MATLAB and has been trained with several MATLAB training functions. Then, an ANN was constructed in FORTRAN using an adaptive algorithm, in which the parameters of momentum and learning rate changed during the learning procedure, in order to optimize the training process. In each case the Artificial Neural Network uses as input variables the following characteristics of the insulator: the diameter, the height, the creepage distance, the form factor and the equivalent salt deposit density and estimates the critical flashover voltage.	en
heal.fullTextAvailability	true
heal.conferenceName	9th WSEAS International Conference on Neural Networks	en
heal.conferenceItemType	poster