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Transformers, reactors and other electrical equipment often work under harmonics and DC-bias working conditions. It is necessary to quickly and accurately simulate the hysteresis characteristics of soft magnetic materials under various excitation conditions in order to achieve accurate calculations of core loss and the optimal design of electrical equipment. Based on Preisach hysteresis model, a parameter identification method for asymmetric hysteresis loop simulation is designed and applied to the simulation of hysteresis characteristics under bias conditions of oriented silicon steel sheets. In this paper, the limiting hysteresis loops of oriented silicon steel sheets are obtained through experiments under different working conditions. The first-order reversal curves(FORCs) with asymmetric characteristics is generated numerically, and then the Everett function is established under different DC bias conditions. The hysteresis characteristics of the oriented silicon steel sheets under harmonics and DC bias are simulated by improving FORCs identification method of the Preisach model. By comparing the results of simulation and experiment, the effectiveness of the proposed method is verified, so as to provide an important reference for material production and application. [ABSTRACT FROM AUTHOR]

A new algorithm for numerical identification of the static Preisach hysteresis model is proposed for more accurate estimation of dc-biasing hysteresis curves and hysteresis loss of the grain-oriented silicon steel sheet. Based on the experimental asymmetric major hysteresis loop under dc-biased magnetization, two sets of first-order reversal curves corresponding to ascending and descending branches, respectively, are numerically generated for construction of the Everett function. According to the equivalent field separation technique, a dynamic hysteresis model is established by parameter extraction of excess loss, and then used for simulation of dc-biasing dynamic hysteretic behaviors. The consistency between the simulated results and the measured ones demonstrates the effectiveness of the proposed method. The effect of dc bias on iron loss is analyzed as well. [ABSTRACT FROM AUTHOR]

The Preisach model is widely used to simulate the magnetic performance of transformers and motors in industrial applications. However, its parameter identification problem is still a complicated task. This paper proposed a new parameter identification method based on the velocity-controlled particle swarm optimization (VCPSO) algorithm combined with the closed-form Everett function. Firstly, the Preisach model is built through the closed-form Everett function, which gives an explicit form Preisach model. Secondly, the Preisach model's parameter identification is realized by using the VCPSO algorithm, which only needs the limiting static hysteresis loop of the material. During the optimization process, the particle velocity is automatically adjusted to avoid falling into the optimal local solution. Finally, the obtained results are compared with the experimental hysteresis loop of the B30P150 silicon steel. [ABSTRACT FROM AUTHOR]

• The developed scalar Preisach model predicts the hysteresis behavior of ferromagnetic material, and experimental hysteresis loops are used to prepare the identification of the Preisach model. • The identification and modelling results obtained by closed algebraic functions and genetic algorithm optimization are not satisfactory compared to the results from the factorable functions. • The Preisach distribution function(PDF) is numerically driven and represents the symmetric and non-exchange-bias hysteresis, including the magnetic domain structure of the materials. The developed scalar Preisach model predicts the hysteresis behavior of the soft magnetic, electrical steel sheet. The experimental hysteresis loops of the electrical steel lamination prepare the Everett function required to predict the hysteresis behavior of the electrical steel lamination. We obtain the Everett function in two ways, from the series of minor loops and a single descending branch of a hysteresis loop. The Everett function has a closed algebraic form in the method using the minor loops. A genetic algorithm carries out the subsequent fitting based on the closed algebraic equation and optimization; however, the calculated flux values have highly deviated from the measured values. In another way, the Everett function consists of numerically driven F s and B factorable functions. The values extracted from a limiting hysteresis loop determine the function F s and B. The predicted values are correct, and the proposed method for determining the Everett function is only valid for soft magnetic materials representing symmetric and non-exchange-bias hysteresis behavior. While the method has application limitations to general magnetic hysteresis, it demonstrates the validity of using a function derived from experimentally measured hysteresis loops rather than a closed-algebraic equation to determine the Everett function. With the Everett surface the Preisach distribution function (PDF) is numerically derived and represents the difference in domain structure between two specimens. [ABSTRACT FROM AUTHOR]