Research on the inverse vector hysteresis model with the deep learning parameter identification algorithm

Abstract

The construction of the vector hysteresis model is significant for the electromagnetic characteristics analysis of electrical equipment. With a deep learning parameter identification algorithm, this paper presents a modeling method of the inverse vector hysteresis model. The vector Everett function is obtained by the alternating hysteresis data. In order to reduce the error of the vector Everett function, the anisotropy compensation coefficient is introduced into the model structure. Based on the white-box model theory, the Stacked Auto-Encoder (SAE) model is used to identify the relevant parameters of the inverse vector hysteresis model. And the parameters of the phase angle compensation function are calculated by the magnetic loss data to further improve the magnetic loss calculation accuracy of the vector model. Compared with the experimental data of rotating magnetization, the fitting result proves the accuracy of the model in a wide range. In addition, the validity of the model is verified by calculating the dynamic magnetic loss.