Prediction of iron losses of wound core distribution transformers based on artificial neural networks

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

dc.contributor.author	Georgilakis, PS	en
dc.contributor.author	Hatziargyriou, ND	en
dc.contributor.author	Doulamis, ND	en
dc.contributor.author	Doulamis, AD	en
dc.contributor.author	Kollias, SD	en
dc.date.accessioned	2014-03-01T11:44:27Z
dc.date.available	2014-03-01T11:44:27Z
dc.date.issued	1998	en
dc.identifier.issn	0925-2312	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36956
dc.subject	Artificial neural networks	en
dc.subject	Individual core specific iron losses	en
dc.subject	Specific iron losses prediction and classification	en
dc.subject	Transformer specific iron losses	en
dc.subject	Wound core distribution transformers	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Electric transformers	en
dc.subject.other	Errors	en
dc.subject.other	Industrial applications	en
dc.subject.other	Iron	en
dc.subject.other	Learning systems	en
dc.subject.other	Quality control	en
dc.subject.other	Distribution transformer	en
dc.subject.other	Iron losses	en
dc.subject.other	Transformer core	en
dc.subject.other	Neural networks	en
dc.subject.other	analytic method	en
dc.subject.other	artificial neural network	en
dc.subject.other	iron transport	en
dc.subject.other	methodology	en
dc.subject.other	prediction	en
dc.subject.other	priority journal	en
dc.subject.other	quality control	en
dc.subject.other	review	en
dc.title	Prediction of iron losses of wound core distribution transformers based on artificial neural networks	en
heal.type	other	en
heal.identifier.primary	10.1016/S0925-2312(98)00071-X	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0925-2312(98)00071-X	en
heal.language	English	en
heal.publicationDate	1998	en
heal.abstract	This paper presents an artificial neural network (ANN) approach to predicting and classifying distribution transformer specific iron losses, i.e., losses per weight unit. The ANN is trained to learn the relationship of several parameters affecting iron losses. For this reason, the ANN learning and testing sets are formed using actual industrial measurements, obtained from previous completed transformer constructions. Data comprise grain oriented steel electrical characteristics, cores constructional parameters, quality control measurements of cores production line and transformers assembly line measurements. It is shown that an average absolute error of 2.32% has been achieved in the prediction of individual core specific iron losses and an error of 2.2% in case of transformer specific losses. This is compared with average errors of 5.7% and 40% in prediction of specific iron losses of individual core and transformer, respectively, obtained by the current practice applying the typical loss curve to the same data.This paper presents an artificial neural network (ANN) approach to predicting and classifying distribution transformer specific iron losses, i.e., losses per weight unit. The ANN is trained to learn the relationship of several parameters affecting iron losses. For this reason, the ANN learning and testing sets are formed using actual industrial measurements, obtained from previous completed transformer constructions. Data comprise grain oriented steel electrical characteristics, cores constructional parameters, quality control measurements of cores production line and transformers assembly line measurements. It is shown that an average absolute error of 2.32% has been achieved in the prediction of individual core specific iron losses and an error of 2.2% in case of transformer specific losses. This is compared with average errors of 5.7% and 4.0% in prediction of specific iron losses of individual core and transformer, respectively, obtained by the current practice applying the typical loss curve to the same data.	en
heal.publisher	Elsevier Sci B.V., Amsterdam, Netherlands	en
heal.journalName	Neurocomputing	en
dc.identifier.doi	10.1016/S0925-2312(98)00071-X	en
dc.identifier.isi	ISI:000077438600003	en
dc.identifier.volume	23	en
dc.identifier.issue	1-3	en
dc.identifier.spage	15	en
dc.identifier.epage	29	en