AI helps reduce transformer iron losses

Author

Georgilakis, Paul ; Hatziargyriou, Nikos ; Paparigas, Dimitrios

Author_Institution

Schneider Electr. AE, Greece

Volume

12

Issue

4

fYear

1999

fDate

10/1/1999 12:00:00 AM

Firstpage

41

Lastpage

46

Abstract

Methods for iron loss reduction during manufacturing of wound-core distribution transformers are presented. More specifically, measurements taken at the first stages of core construction are effectively used, in order to minimize iron losses of transformer (final product). To optimally exploit the measurements (feedback), artificial intelligence methods are applied. It is shown that intelligent systems are able to learn and interpret several variations of the same conditions, thus helping in predicting iron losses with increased accuracy

Keywords

artificial intelligence; distribution networks; losses; manufacture; power engineering computing; power transformers; transformer cores; transformer windings; AI; artificial intelligence methods; core construction measurements; intelligent systems; iron loss prediction accuracy; iron loss reduction; manufacturing; wound-core distribution transformers; Assembly; Conducting materials; Dielectric losses; Iron; Lamination; Magnetic cores; Power transformer insulation; Production; Transformer cores; Wounds;

fLanguage

English

Journal_Title

Computer Applications in Power, IEEE

Publisher

ieee

ISSN

0895-0156

Type

jour

DOI

10.1109/67.795137

Filename

795137