Title :
AI helps reduce transformer iron losses
Author :
Georgilakis, Paul ; Hatziargyriou, Nikos ; Paparigas, Dimitrios
Author_Institution :
Schneider Electr. AE, Greece
fDate :
10/1/1999 12:00:00 AM
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;
Journal_Title :
Computer Applications in Power, IEEE
