Artificial intelligence combined with hybrid FEM-BE techniques for global transformer optimization

The aim of the transformer design optimization is to define the dimensions of all the parts of the transformer, based on the given specification, using available materials economically in order to achieve lower cost, lower weight, reduced size, and better operating performance. In this paper, a hybrid artificial intelligence/numerical technique is proposed for the selection of winding material in power transformers. The technique uses decision trees and artificial neural networks for winding material classification, along with finite-element/boundary element modeling of the transformer for the calculation of the performance characteristics of each considered design. The efficiency and accuracy provided by the hybrid numerical model render it particularly suitable for use with optimization algorithms. The accuracy of this method is 96% (classification success rate for the winding material on an unknown test set), which makes it very efficient for industrial use.