Modelling of inrush current in transformers using inverse Jiles–Atherton hysteresis model with a Neuro-shuffled frog-leaping algorithm approach

This study presents a novel approach for modelling inrush current transient in power transformers taking into account the hysteresis effect. Inverse Jiles–Atherton (JA) model is used to represent the hysteresis phenomenon in the magnetic core. The parameters of this model are optimally determined using inrush current measurements by shuffled frog-leaping algorithm (SFLA). Then, an adaptive technique to enhance the accuracy of the proposed model for simulation of inrush current in other possible conditions is proposed. The method is based on artificial neural network which is used for updating the inverse JA hysteresis model parameters in each hysteresis loop during a power frequency cycle. SFLA optimisation is used for accurate parameter determination from measured inrush currents in training stage. The measurements are performed on a single-phase power transformer and the results verify the performance of the proposed neuro-SFLA approach.