A hybrid Jiles-Atherton and prisach model of dynamic magnetic hysteresis based on backpropagation neural networks

Ferromagnetic materials are widely used for magnetic cores in electromagnetic devices such as inductors, transformers, generators, and motors. In a magnetic core, as the magnetic field varies with time, core loss is generated due to magnetic hysteresis and eddy currents. For performance analysis and design optimization of electromagnetic devices, it is essential to model the dynamic magnetization processes and associated core losses accurately. This paper proposes a hybrid model of dynamic magnetic hysteresis, which incorporates the effects of both hysteresis and eddy currents, by combining the dynamic Jiles-Atherton and Preisach models based on backpropagation neural networks. This model can accurately reproduce the dynamic hysteresis loops and core losses under different excitations. The numerical simulations are verified by experimental measurements.

Automated summary

A hybrid model of dynamic magnetic hysteresis combining the Jiles-Atherton and Preisach models based on backpropagation neural networks is proposed in this paper. This model accurately reproduces dynamic hysteresis loops and core losses under different excitations, validated by experimental measurements.