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This paper aims to compare different static history-independent hysteresis models (mathematical-, behavioural- and physical-based ones) and a history-dependent hysteresis model in terms of parameter identification effort and accuracy.

The discussed models were tested for distorted-excitation waveforms to explore their predictions of complex magnetization curves. Static hysteresis models were evaluated by comparing the calculated and measured major and minor static hysteresis loops.

The analysis shows that the resulting accuracy of the different hysteresis models is strongly dependent on the excitation waveform, i.e. smooth excitations, distorted flux waveforms, transients or steady-state regimes. Obtained results show significant differences between predictions of discussed static hysteresis models.

The general aim was to identify the models on a very basic and limited set of measured data, i.e. if possible using only the measured major static loop of the material. The quasi-static major hysteresis loop was measured at B_max = 1.5 T.

The presented analysis allows selection of the most-suited hysteresis model for the sought-for application and appraisal of the individual limitations.

The presented analysis shows differences in intrinsic mechanisms to predict magnetization curves of the majority of the well-known static hysteresis models. The results are essential when selecting the most-suited hysteresis model for a specific application.

This paper aims to deal with direct torque controller when the five-phase induction motor drive in faulty operation. Precisely, open-phase fault condition is contemplated. Also, the DTC is combined with a speed-adaptive variable-structure observer based on sliding mode observer.

Two novel features are presented. First, the concept of the virtual voltage vector is presented, which eliminates low-frequency harmonic currents and simplifies analysis. Second, speed information is introduced into the selection of the inverter states.

Direct torque control (DTC) is largely used in traditional three-phase drives as a backup to rotor-stator flux-oriented methods. The classic DTC strategy was primarily designed on the base of hysteresis controllers to control two independent variables (speed, torque and flux). Due to the additional degrees of freedom offered by multiphase machine, extensive works have been extended on the ensemble five-phase drives in healthy operation. In addition, the ability to continue the operation in faulty conditions is considering one of the main advantages of multiphase machines. One can find in the literature different approaches treating this subject. The applicability of DTC after the appearing of a fault has not been enclosed in the literature.

Theoretical development is presented in details followed by simulation results using Matlab/Simulink to analyze the performance of the drive, comparing with the behavior during healthy situation.

Calculation of flux distribution in three‐phase three‐limb transformer core has been made using 2D finite element method. The calculation has been performed with and without taking into account lthe hysteresis of magnetic material. Anisotropy of magnetic material has been modelled through the so‐called elliptic permeability model. Comparison of results of calculation and results of measurement has been performed for a scale model of transformer core. An agreement better than 3 per cent has been obtained, for calculated and measured results of third harmonics and of flux distortion factors, when hysteresis has been taken into account. The CPU‐time was 6–10 times longer when hysteresis has been included into calculation.

The classical Preisach model and its modifications are one of the most generally applied simulations to model the behavior of magnetic materials, to describe hysteresis phenomena and different properties, as noncongruent minor loops, frequency dependence, temperature dependence, accommodation, and so on. Artificial neural networks (NNs) are widely used in fields of research where the solution of problems with conventional methods on traditional computers is very difficult to work out, for example system identification, modeling and function approximation. NNs can be considered as universal approximation for functions based on the theorem of Kolmogorov‐Arnold. In this paper a new NN model of scalar hysteresis characteristics is introduced. The examined method is built on the function approximation and continuous interpolation capability of NNs. The anhysteretic magnetization curve and a set of the ascending and a set of the descending first order reversal branches can be stored in a system of three neural networks. Different properties of magnetic materials can be simulated by a simple knowledge‐based algorithm. Value of differential susceptibility can be expressed in analytical form. Finally hysteresis characteristics predicted by the introduced model are compared with the results of the Preisach simulation.

This paper starts with the description of a purely mathematical model of the saturation curve and the hysteresis loop based on the fundamental similarities between the Langevin function the specified T(x) function and the sigmoid shape. The T(x) function which is composed of tangent hyperbolic and linear functions with its free parameters can describe the regular anhysteretic magnetisation curve. Developed from this function the model describes not only the regular hysteresis loop but also the biased and other minor loops like the ones produced by the interrupted and reversed magnetisation process and the open “loops” created by a piecewise monotonic magnetising field input of diminishing amplitude. The remanent magnetism as the function of the interrupted field co‐ordinates is predicted by the model in this mathematical form for the first time. The model presented here is based on the principle that all processes follow the shape of the T(x) function describing the shape of the major hysteresis loop of the ferromagnetic specimen under investigation. The model is also applicable to hysteretic processes in other fields.

The purpose of this paper is to implement the Anderson acceleration for different formulations of eletromagnetic nonlinear problems and analyze the method efficiency and strategies to obtain a fast convergence.

The paper is structured as follows: the general class of fixed point nonlinear problems is shown at first, highlighting the requirements for convergence. The acceleration method is then shown with the associated pseudo-code. Finally, the algorithm is tested on different formulations (finite element, finite element/boundary element) and material properties (nonlinear iron, hysteresis models for laminates). The results in terms of convergence and iterations required are compared to the non-accelerated case.

The Anderson acceleration provides accelerations up to 75 per cent in the test cases that have been analyzed. For the hysteresis test case, a restart technique is proven to be helpful in analogy to the restarted GMRES technique.

The acceleration that has been suggested in this paper is rarely adopted for the electromagnetic case (it is normally adopted in the electronic simulation case). The procedure is general and works with different magneto-quasi static formulations as shown in the paper. The obtained accelerations allow to reduce the number of iterations required up to 75 per cent in the benchmark cases. The method is also a good candidate in the hysteresis case, where normally the fixed point schemes are preferred to the Newton ones.

The purpose of this paper is to propose a chaotic optimization method for identifying the parameters of the Jiles–Atherton (J-A) hysteresis model.

The J-A model has five parameters which are assigned with physical meaning and whose determination is demanding. To determine these parameters, the fitness function, which represents the difference between the measured and the modeled hysteresis loop, is formed. Optimal parameter values are the values that minimize the fitness function.

The parameters of J-A model for three magnetic materials are determined. The model with the optimal parameters is validated using measured data and comparison with particle swarm optimization algorithm, genetic algorithm, pattern search and simulated annealing algorithm. The results show that the proposed method provides better agreement between measured and modeled hysteresis loop than other methods used for comparison. The proposed method is also suitable for simultaneous optimization of multiple hysteresis loops.

Chaotic optimization method is implemented for the first time for J-A model parameter identification. Numerical comparisons with results obtained with other optimization algorithms demonstrate that this method is a suitable alternative in parameters identification of J-A hysteresis model. Furthermore, this method is easy to implement and set up.

The purpose of this paper is to examine the effect of varying compaction pressure on magnetic properties of self-developed soft magnetic composite (SMC) cores. The change in shape of ferromagnetic hysteresis curves has – in turn – the impact on the values of hysteresis model parameters. The phenomenological GRUCAD model is chosen for description of hysteresis curves.

Several cylinder-shaped cores have been made from a mixture of iron powder and suspense polyvinyl chloride using a hydraulic press with a form and a band with a thermocouple for controlling heat treatment conditions. The only varying parameter in the study is the compaction pressure. The magnetic properties of developed cores have been measured using a computer-acquisition card and LabView software. The obtained hysteresis curves are fitted to the equations of the phenomenological GRUCAD model. This description is compliant with the laws of irreversible thermodynamics. The variations of model parameters are presented as functions of compacting pressure.

The compaction pressure has a significant impact on magnetic properties of self-developed SMC cores. The paper provides a number of charts useful for checking how the parameters of the hysteresis model are affected.

The present paper is limited to modelling symmetrical loops only. Description of more complex magnetization cycles is postponed to another, forthcoming paper.

The GRUCAD hysteresis model may be a useful tool for the designers of magnetic circuits. Its parameters depend on the processing conditions (in this study – the compaction pressure) of the SMC cores.

Modelling of magnetic properties of SMC cores has been carried so far using some well-known description like Preisach, Takács and Jiles–Atherton proposals. The GRUCAD model has a number of advantages, and it may be a useful alternative to the latter formalism. So far it has been used for description of hysteresis curves in conventional materials like non-oriented and grain-oriented electrical steels. In the present work, it is applied to novel SMC materials.

The aim of the paper is to provide a simple and reliable hysteresis model for prediction of magnetization curves of a resistance spot welding transformer (RSWT) core, operating in a wide range of flux densities and excitation frequencies.

The hysteresis model considered in the paper is the T(x) description advanced by J. Takács. Three options to extend the model to the dynamic magnetization conditions are considered. The excitation conditions differ from those prescribed by international standards.

The quasi‐static Takács model combined with a fractional viscosity equation similar to that proposed by S.E. Zirka outperforms other considered options. The effect of eddy currents may be considered as a disturbance factor to the frequency‐independent quasi‐static hysteresis loop.

The combined approach yields in most cases a satisfactory agreement between theory and experiment. For highest frequency considered in the paper (1 kHz) excessive “heels” were observed in the modelled loops. This artifact may be reduced by the introduction of a more complicated relationship for the viscous term. Future work shall be devoted to this issue.

The combined Takács‐Zirka model is a useful tool for prediction of magnetization curves of a RSWT core in a wide range of flux densities and excitation frequencies.

The usefulness of the Takács description has been verified in a practical application. The model is able to predict magnetization curves under non‐standard excitation conditions.

This paper deals with the numerical modelling of electromagnetic losses in electrical machines, using electromagnetic field computations, combined with advanced material characterisations. The paper gradually proceeds to the actual reasons why the building factor, defined as the ratio of the measured iron losses in the machine and the losses obtained under standard conditions, exceeds the value of 1.