Abstract
A new model of hysteresis based on hypergeometric functions is presented. The model is based on a class of parameterized functions with two free parameters, which contains the Takács model as a special case. With a suitable choice of the parameters, a model of hysteresis can be constructed, which shows improved conformance to experimental data, such as steep and narrow loops obtained from grain-oriented electric steel. The performance of our model was tested and verified on our measurement data, as well as data from other sources. It retains the advantages such as simple numerical implementation and parameter estimation while offering an increase in accuracy.
Highlights
Hysteresis loops are among the most significant characteristics of ferromagnetic materials
Theoretical hysteresis models are used in numerical simulations and hysteretic loss calculations, for example, in transformers and electric motors
It is a common goal of researchers to develop hysteresis models that closely approximate the experimental data obtained in measurements
Summary
Hysteresis loops are among the most significant characteristics of ferromagnetic materials. Theoretical hysteresis models are used in numerical simulations and hysteretic loss calculations, for example, in transformers and electric motors. It is a common goal of researchers to develop hysteresis models that closely approximate the experimental data obtained in measurements. Grain-oriented electrical steel, as used in transformer cores, has a high permeability and a small hysteresis area, which causes small energy dissipation per cycle Such materials exhibit highly steep, gooseneck, and narrow waist rolling direction hysteresis loops (see Ref. 1). Dynamic extensions to the Takács model were studied by Chwastek, and in Ref. 8, some of its modifications that enable hysteresis simulation to represent the actual data more closely are examined. It turned out that this approach is adequate because the shape of the model curve is convenient, and the parameters are determined
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