A synergetic neural network-genetic scheme for optimal transformer construction

Doulamis, ND; Doulamis, AD; Georgilakis, PS; Kollias, SD; Hatziargyriou, ND

dc.contributor.author	Doulamis, ND	en
dc.contributor.author	Doulamis, AD	en
dc.contributor.author	Georgilakis, PS	en
dc.contributor.author	Kollias, SD	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T01:17:24Z
dc.date.available	2014-03-01T01:17:24Z
dc.date.issued	2002	en
dc.identifier.issn	1069-2509	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14510
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0036143207&partnerID=40&md5=5844c1c09fe02776a2bddbe57cfb27a7	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Multidisciplinary	en
dc.subject.other	Eddy currents	en
dc.subject.other	Electric losses	en
dc.subject.other	Electric transformers	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Hysteresis	en
dc.subject.other	Iron losses	en
dc.subject.other	Neural networks	en
dc.title	A synergetic neural network-genetic scheme for optimal transformer construction	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2002	en
heal.abstract	In this paper, a combined neural network and an evolutionary programming scheme is proposed to improve the quality of wound core distribution transformers in an industrial environment by exploiting information derived from both the construction and transformer design phase. In particular, the neural network architecture is responsible for predicting transformer iron losses prior to their assembly, based on several actual core measurements, transformer design parameters and the specific core assembling. A genetic algorithm is applied to estimate the optimal core arrangement, (i.e. the way of core assembling) that yields a set of three-phase transformers of minimal iron losses. The minimization is performed by exploiting information derived from the neural network model resulting in a synergetic neural network-genetic algorithm Scheme. After the transformer construction, the prediction accuracy of the neural network model is evaluated. If accuracy is poor, a weight adaptation algorithm is applied to improve the prediction performance. For the weight updating, bath the current and the previous network knowledge are taken into account. Application of the proposed neural network-genetic algorithm scheme to our industrial environment indicates a significant reduction in the variation between the actual and the designed transformer iron losses. This further leads to a reduction of the production cost since a smaller safety margin can be used for the transformer design.	en
heal.publisher	IOS PRESS	en
heal.journalName	Integrated Computer-Aided Engineering	en
dc.identifier.isi	ISI:000173738400003	en
dc.identifier.volume	9	en
dc.identifier.issue	1	en
dc.identifier.spage	37	en
dc.identifier.epage	56	en