Artificial intelligence combined with hybrid FEM-BE techniques for global transformer optimization

Amoiralis, EI; Georgilakis, PS; Tsili, MA; Kladas, AG

dc.contributor.author	Amoiralis, EI	en
dc.contributor.author	Georgilakis, PS	en
dc.contributor.author	Tsili, MA	en
dc.contributor.author	Kladas, AG	en
dc.date.accessioned	2014-03-01T02:43:57Z
dc.date.available	2014-03-01T02:43:57Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31569
dc.subject	Artificial Intelligent	en
dc.subject	Attribute Selection	en
dc.subject	Boundary Element	en
dc.subject	Design Optimization	en
dc.subject	Finite Element	en
dc.subject	Numerical Technique	en
dc.subject	Power Transformer	en
dc.subject	Success Rate	en
dc.subject	Decision Tree	en
dc.subject	Neural Network	en
dc.subject.other	Boundary element modeling	en
dc.subject.other	Power transformers	en
dc.subject.other	Winding material	en
dc.subject.other	Boundary element method	en
dc.subject.other	Electric transformers	en
dc.subject.other	Finite element method	en
dc.subject.other	Machine design	en
dc.subject.other	Mathematical models	en
dc.subject.other	Optimization	en
dc.subject.other	Artificial intelligence	en
dc.title	Artificial intelligence combined with hybrid FEM-BE techniques for global transformer optimization	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/CEFC-06.2006.1632921	en
heal.identifier.secondary	http://dx.doi.org/10.1109/CEFC-06.2006.1632921	en
heal.identifier.secondary	1632921	en
heal.publicationDate	2006	en
heal.abstract	The aim of the transformer design optimization is to define in detail the dimensions of all the parts of the transformer, based on the given specification, using available materials economically in order to achieve lower cost, lower weight, reduced size and better operating performance. In this paper, a hybrid artificial intelligence - numerical technique is proposed for the selection of winding material in power transformers. The technique uses decision trees for attribute selection and neural networks for winding material classification, along with finite element - boundary element modeling of the transformer for the calculation of the performance characteristics of each considered design. The accuracy of the proposed method is 95.5% (classification success rate for the winding material on an unknown test set), which makes it very efficient for industrial use. ©2006 IEEE.	en
heal.journalName	12th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2006	en
dc.identifier.doi	10.1109/CEFC-06.2006.1632921	en