Automatic Learning Techniques for on-line Control and Optimization of Transformer Core Manufacturing - Industry Applications Conference, 1999. Thirty-Fourth IAS Annual Meeting. Conference Record of the

In this paper, a novel computer based learning framework that bas been developed and applied for the on-line control and optimization of transformer core manufacturing process is presented. Tbe proposed framework aims at predicting core losses of wound core distribution transformers at the early stages of transformer construction. Moreover. it is used to improve the grouping process of the individual cores by reducing iron losses of assembled transformers. Three different automatic learning techniques (namely decision trees, artificial neural networks and genetic algorithms) are combined and their relevant features are exploited. fitdm Terns Decision trees, neural networks, genetic algorithms, inieIJigent core lass modeling.