Neural Network Modeling of Complex Hysteresis Loops in Ferromagnetic Materials

Abstract

To investigate the impact of diverse multivariate mixing excitation conditions on the hysteresis loop of ferromagnetic materials, this study initially constructs a magnetic performance testing system for electrical steel. This system is capable of generating mixed excitation utilizing a standard Epstein square-circle setup. Subsequently, the study measures the magnetic properties of the oriented silicon steel sheets at various mixing AC frequencies of the hysteresis loop data. Secondly, a hybrid network model integrating a convolutional neural network (CNN) and a bi-directional long short-term memory network (BiGRU), augmented with an attention mechanism (AM), is proposed and utilized for predicting the hysteresis properties of oriented silicon steel wafers subjected to compound mixed-frequency excitation. The model utilizes CNN to extract high-dimensional data features reflecting the hysteresis characteristics of the loop, BiGRU to capture the temporal evolution patterns of the key feature vectors, an AM to weigh the feature parameters and emphasize the key features, and a Bayesian optimization (BO) algorithm based on neural network hyperparameters for automatic selection, enhancing prediction accuracy. In comparison with experimental observations, the method accurately predicts material hysteresis properties under non-sinusoidal complex excitation conditions, outperforming existing deep-learning network models. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.