* Corresponding author. E-mail: pgeorg@power.ece.ntua.gr Neurocomputing 23 (1998) 15—29 Prediction of iron losses of wound core distribution transformers based on artificial neural networks P.S. Georgilakis*, N.D. Hatziargyriou, N.D. Doulamis, A.D. Doulamis, S.D. Kollias  Schneider Electric AE, Elvim Plant, P.O. Box 59 GR-32011, Inofyta Viotia, Greece  Electric Energy Systems Laboratory, Department of Electrical and Computer Engineering, National Technical University of Athens, 42 Patission Str., 106 82 Athens, Greece  Digital Signal Processing Laboratory, Department of Electrical and Computer Engineering, National Technical University of Athens, Greece Received 3 November 1997; accepted 15 July 1998 Abstract This paper presents an artificial neural network (ANN) approach to predicting and classify- ing 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 produc- tion 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.  1998 Elsevier Science B.V. All rights reserved. Keywords: Wound core distribution transformers; Individual core specific iron losses; Trans- former specific iron losses; Artificial neural networks; Specific iron losses prediction and classification 0925-2312/98/$ — see front matter  1998 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 5 - 2 3 1 2 ( 9 8 ) 0 0 0 7 1 - X