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

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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


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