Application of artificial neural network methods for the lightning performance evaluation of Hellenic high voltage transmission lines

Ekonomou, L; Gonos, IF; Iracleous, DP; Stathopulos, IA

dc.contributor.author	Ekonomou, L	en
dc.contributor.author	Gonos, IF	en
dc.contributor.author	Iracleous, DP	en
dc.contributor.author	Stathopulos, IA	en
dc.date.accessioned	2014-03-01T01:25:56Z
dc.date.available	2014-03-01T01:25:56Z
dc.date.issued	2007	en
dc.identifier.issn	0378-7796	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17822
dc.subject	Backflashover failure rate	en
dc.subject	Feed-forward neural networks	en
dc.subject	High voltage transmission lines	en
dc.subject	Lightning performance	en
dc.subject	Radial basis function neural networks	en
dc.subject	Shielding failure rate	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Computer simulation	en
dc.subject.other	Electric potential	en
dc.subject.other	Electric power utilization	en
dc.subject.other	Learning algorithms	en
dc.subject.other	Lightning	en
dc.subject.other	Neural networks	en
dc.subject.other	Transfer functions	en
dc.subject.other	Backflashover failure rate	en
dc.subject.other	Feed-forward neural networks	en
dc.subject.other	High voltage transmission lines	en
dc.subject.other	Lightning performances	en
dc.subject.other	Radial basis function neural networks	en
dc.subject.other	Shielding failure rate	en
dc.subject.other	Electric lines	en
dc.title	Application of artificial neural network methods for the lightning performance evaluation of Hellenic high voltage transmission lines	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.epsr.2006.01.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.epsr.2006.01.005	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	Feed-forward (FF) artificial neural networks (ANN) and radial basis function (RBF) ANN methods were addressed for evaluating the lightning performance of high voltage transmission lines. Several structures, learning algorithms and transfer functions were tested in order to produce a model with the best generalizing ability. Actual input and output data, collected from operating Hellenic high voltage transmission lines, as well as simulated output data were used in the training, validation and testing process. The aims of the paper are to describe in detail and compare the proposed FF and RBF ANN models, to state their advantages and disadvantages and to present results obtained by their application on operating Hellenic transmission lines of 150 kV and 400 kV. The ANN results are also compared with results obtained using conventional methods and real records of outage rate showing a quite satisfactory agreement. The proposed ANN methods can be used by electric power utilities as useful tools for the design of electric power systems, alternative to the conventional analytical methods. (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.2006.01.005	en
dc.identifier.isi	ISI:000240907500006	en
dc.identifier.volume	77	en
dc.identifier.issue	1	en
dc.identifier.spage	55	en
dc.identifier.epage	63	en