A New Technique for Modeling Hysteresis Phenomenon in Soft Magnetic Materials

Abstract

A major part of power systems consists of ferromagnetic materials, and investigation of their parameters and magnetic characteristics in various operating conditions is necessary. Hysteresis in ferromagnetic materials is a complicated physical phenomenon, and its modeling is a challenging problem. In fact, the basic problem in this modeling is that for a given value of the magnetic field intensity, there are unlimited possible magnetic flux density values that depend on the history of the material. A class of the hysteresis models as physical models is expressed based on the physics theories. In the present article, a new model for hysteresis phenomenon is proposed. This model is a combination of mathematical equations and models that have been obtained based on the experimental observations and a series of the magnetic paths macroscopic properties extracted from the Preisach model. The proposed model generates symmetrical and asymmetrical hysteresis loops with high accuracy. In this model, an artificial neural network is used to present the descending path of the major DC hysteresis loop and initial magnetization curve based on a set of experimental data. In this model, it is necessary to extract some major properties of the magnetic paths from the well-known Preisach model. These features and also the proposed mathematical equations, obtained based on the experimental observations, are then used to generate the symmetrical and asymmetrical hysteresis paths.