NEURAL-NETWORK FOR REPRESENTATION OF HYSTERESIS LOOPS

The paper presents a mapping model for the representation of symmetric al B/H characteristics over the whole of the B/H plane. based on neura l networks taught by backpropagation. The model could not be achieved accurately by just using a symmetrical saturated hysteresis loop to si mulate a smaller hysteresis loop, 11 experimentally obtained hysteresi s loops from over the whole of the B/H plane were used to train neural networks. These are good enough to represent all nonlinear hysteresis characteristics to meet the needs of an engineering calculation, e.g. a transient performance analysis of a current transformer or voltage transformer. The simulation accuracy depends ultimately on the accurac y of the experimental data. The computed results using this model have shown a good agreement with measured data.