Hybrid intelligent hysteresis model based on DBN-DNN algorithm and fusion Preisach operator

Abstract

The hysteresis modeling of ferromagnetic materials in electrical equipment is one of the basic theoretical studies in the field of electrical engineering. Long training time and poor accuracy are the main challenges of the traditional hysteresis models. In this paper, a Hybrid Intelligent Hysteresis Model (HIHM) is proposed by combining the hysteresis operator space theory and Deep Belief Networks-Deep Neural Network (DBN-DNN) algorithm. The Preisach Fusion Operator (PFO) is constructed by fusing multiple traditional Preisach operators with coefficients satisfying the Gaussian distribution. And multiple PFOs are used to construct an operator space to create the high-dimensional operator data. By the operator space, the nonlinear hysteresis relationship in the hysteresis data is transformed into a nonlinear mapping between the operator data and the output of the model. The DBN-DNN model is used to characterize this nonlinear mapping. The structure of the HIHM is determined by taking the output of the operator space as the input of the DBN model. The generation of first-order reversal curve proves that the model has certain practicality. In addition, the validity and generalization of this model is verified through the simulation with experimental data of non-orientation (NO) silicon steel B35A210 at quasi-static frequency.