A novel iron loss reduction technique for distribution transformers based on a combined genetic algorithm-neural network approach

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

dc.contributor.author	Georgilakis, PS	en
dc.contributor.author	Doulamis, ND	en
dc.contributor.author	Doulamis, AD	en
dc.contributor.author	Hatziargyriou, ND	en
dc.contributor.author	Kollias, SD	en
dc.date.accessioned	2014-03-01T01:16:02Z
dc.date.available	2014-03-01T01:16:02Z
dc.date.issued	2001	en
dc.identifier.issn	1094-6977	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13891
dc.subject	Core grouping process	en
dc.subject	Decision trees	en
dc.subject	Genetic algorithms	en
dc.subject	Intelligent core loss modeling	en
dc.subject	Iron loss reduction	en
dc.subject	Neural networks	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Computer Science, Cybernetics	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.other	Core grouping process	en
dc.subject.other	Decision trees	en
dc.subject.other	Intelligent core loss modeling	en
dc.subject.other	Iron loss reduction	en
dc.subject.other	Adaptive algorithms	en
dc.subject.other	Cost effectiveness	en
dc.subject.other	Decision theory	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Magnetic cores	en
dc.subject.other	Magnetic leakage	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Electric transformers	en
dc.title	A novel iron loss reduction technique for distribution transformers based on a combined genetic algorithm-neural network approach	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/5326.923265	en
heal.identifier.secondary	http://dx.doi.org/10.1109/5326.923265	en
heal.language	English	en
heal.publicationDate	2001	en
heal.abstract	This paper presents an effective method to reduce the iron losses of wound core distribution transformers based on a combined neural network- genetic algorithm approach. The originality of the work presented in this paper is that it tackles the iron loss reduction problem during the transformer production phase, while previous works were concentrated on the design phase. More specifically, neural networks effectively use measurements taken at the first stages of core construction in order to predict the iron losses of the assembled transformers, while genetic algorithms are used to improve the grouping process of the individual cores by reducing iron losses of assembled transformers. The proposed method has been tested on a transformer manufacturing industry. The results demonstrate the feasibility and practicality of this approach. Significant reduction of transformer iron losses is observed in comparison to the current practice leading to important economic savings for the transformer manufacturer.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews	en
dc.identifier.doi	10.1109/5326.923265	en
dc.identifier.isi	ISI:000168755000002	en
dc.identifier.volume	31	en
dc.identifier.issue	1	en
dc.identifier.spage	16	en
dc.identifier.epage	34	en