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The electrical machines connected to modern electric power grids are non-sinusoidal excited, and their augmented losses, including iron losses, limit their working characteristics. This paper aims to propose a prediction method for iron losses in non-oriented grains (NO) FeSi sheets under non-sinusoidal voltage, involving an inverse classical Preisach hysteresis model and the time-integration of each loss component.

The magnetic history management in inverse Preisach model is optimized and a numerical Everett function is identified from measured symmetrical hysteresis cycles. The experimental data for sinusoidal waveforms obtained by a single sheet tester were also used to identify the parameters involved in Bertotti’ losses separation method. The non-sinusoidal magnetic induction waveform, corresponding to a measured voltage in an industrial electrical grid, was the input for Preisach model, the output magnetic field being accurately computed. The hysteresis, classical and excess losses are calculated by time-integration and the total losses are compared with those obtained for sinusoidal excitation.

The proposed method allows to estimate the iron losses for non-sinusoidal magnetic induction, using carefully identified parameters of FeSi NO sheets, using experimental data from sinusoidal regimes.

The method accuracy is assured by using a numerical Everett function, a variable Preisach grid step (adapted for the high non-linearity of FeSi sheets) and high-order fitting polynomials for the microscopic parameters involved in the excess loss estimation. The procedure allows a better design of magnetic cores and an improved estimation of the electric machine derating for non-sinusoidal voltages.

Piezoelectric actuators (PEAs) exhibit hysteresis nonlinearity in open-loop operation, which may lead to unwanted inaccuracy and limit system performance. Classical Preisach model is widely used for representing hysteresis but it requires a large number of first-order reversal curves to ensure the model accuracy. All the curves may not be obtained due to the limitations of experimental conditions, and the detachment between the major and minor loops is not taken into account. The purpose of this paper is to propose a modified Preisach model that requires relatively few measurements and that describes the detachment, and then to implement the inverse of the modified model for compensation in PEAs.

The classical Preisach model is modified by adding a derivative term in parallel. The derivative gain is adjusted to an appropriate value so that the measured and predicted hysteresis loops are in good agreement. Subsequently, the new inverse model is similarly implemented by adding another derivative term in parallel with the inverse classical Preisach model, and is then inserted in open-loop operation to compensate the hysteresis. Tracking control experiments are conducted to validate the compensation.

The hysteresis in PEAs can be accurately and conveniently described by using the modified Preisach model. The experimental results prove that the hysteresis effect can be nearly completely compensated.

The proposed modified Preisach model is an effective and convenient mean to characterize accurately the hysteresis. The compensation method by inserting the inverse modified Preisach model in open-loop operation is feasible in practice.

The losses incurred in ferromagnetic materials under PWM excitations must be predicted accurately to optimize the design of modern electrical machines. The purpose of this paper is to employ mathematical hysteresis models (i.e. classical Preisach model) to predict iron losses in electrical steels under PWM excitation without compromising the computational complexity of the model.

In this paper, a novel approach based on the dynamic inverse Preisach model is proposed to model the iron losses. The PWM magnetic flux density waveform is decomposed into its harmonic component using Fourier series and a weighted Everett function is computed based on these harmonic components. The Preisach model is applied for the given flux waveform and results are validated against the measurements.

The paper predicts the total iron loss by computing a weighted Everett function based on the harmonics present in PWM waveform. Moreover, it formulates the possibility of utilizing the classical Preisach model to predict iron losses under PWM excitation.

The approach is still limited in terms of its application at high frequencies. This work may eventually lead toward the accurate prediction of iron loss under PWM excitation in electromagnetic machine design.

The paper provides a simple approach applying the Preisach model for the prediction of iron losses under PWM excitation. The proposed approach does not require additional experimental data beyond B-H loops measured under sinusoidal excitation.

A novel approach is presented to incorporate the frequency dependence into a static inverse Preisach model. The approach extends the ability of the static Preisach model to compute total iron loss under PWM excitation using a weighted Everett function.

In the modeling of transducers, especially magnetic transducers, hysteresis may affect performance. Hysteresis models have been improved greatly and are capable of modeling a large variety of rate‐independent phenomena, and are capable of describing minor loops. Of these, the most useful are: the Preisach model, the play model, and the stop model. Coupling these purely magnetic models with other phenomena, such as magnetostriction, enhances the model’s usefulness for transducer applications. This paper will discuss the conditions under which these models may be inverted, and for the invertible media, a technique for inverting them.

The purpose of this paper is to present a Preisach model to simulate the vector hysteresis properties of ferromagnetic materials.

The vector behavior has been studied at low frequency applying a single‐sheet tester with a round‐shaped specimen, and the locus of the magnetic flux density vector has been controlled by a digital measurement system. An inverse vector Preisach hysteresis model has been developed and identified by using the measured data.

Finally, the inverse model has been inserted into a finite element procedure through the combination of the fixed point technique and the reduced magnetic scalar potential formulation. The developed single‐sheet tester measurement system has been simulated. The applicability of the realized measurement system as well as the developed model has been proven by comparing measured and simulated results.

The identification technique is original, based on a previous work of the author.

In a model resulting from Maxwell's equations with a constitutive law using Preisach operators for incorporating magnetization hysteresis, this paper aims at identifying the hysteresis operator, i.e. the Preisach weight function, from indirect measurements.

Dealing with a nonlinear inverse problem, one has to apply iterative methods for its numerical solution. For this purpose several approaches are proposed based on fixed point or Newton type ideas. In the latter case, one has to take into account nondifferentiability of the hysteresis operator. This is done by using differentiable substitutes or quasi‐Newton methods.

Numerical tests with synthetic data show that fixed point methods based on fitting after a full forward sweep (alternating iteration) and Newton type iterations using the hysteresis centerline or commutation curve exhibit a satisfactory convergence behavior, while fixed point iterations based on subdividing the time interval (Kaczmarz) suffer from instability problems and quasi Newton iterations (Broyden) are too slow in some cases.

Application of the proposed methods to measured data will be the subject of future research work.

The proposed methodologies allow to determine material parameters in hysteresis models from indirect measurements.

Taking into account the full PDE model, one can expect to get accurate and reliable results in this model identification problem. Especially the use of Newton type methods – taking into account nondifferentiability – is new in this context.

The purpose of this paper is to develop a viscous-type frequency dependent scalar Preisach hysteresis model and to identify the model using measured data and nonlinear numerical field analysis. The hysteresis model must be fast and well applicable in electromagnetic field simulations.

Iron parts of electrical machines are made of non-oriented isotropic ferromagnetic materials. The finite element method (FEM) is usually applied in the numerical field analysis and design of this equipment. The scalar Preisach hysteresis model has been implemented for the simulation of static and dynamic magnetic effects inside the ferromagnetic parts of different electrical equipment.

The comparison between measured and simulated data using a toroidal core shows a good agreement. A modified nonlinear version of TEAM Problem No. 30.a is also shown to test the hysteresis model in the FEM procedure.

The dynamic model is an extension of the static one; an extra magnetic field intensity term is added to the output of the static inverse model. This is a viscosity-type dynamic model. The fixed-point method with stable scheme has been realized to take frequency dependent anomalous losses into account in FEM. This scheme can be used efficiently in the frame of any potential formulations of Maxwell's equations.

A new method for the determination of the classical Preisach’s model distribution function is developed. The proposed method determines numerically the distribution function from classical experimental measurements and does not make any assumption concerning the material type. The Preisach’s triangle is discretised in a finite set of cells (about 200 cells are needed). Two ways for the determination of the discretised distribution function are presented. The first assumes constant distribution function value in each cell. The second determines the nodal values of the discretised distribution function and uses a bilinear interpolation technique to obtain the distribution function in any position of the Preisach’s triangle. We also show that the proposed method can also be used to model the inverse distribution function. The comparison between modelled and experimental hysteresis curves for both major and minor cycles have shown the effectiveness of the proposed method.

The purpose of this study is to model the global dynamic hysteresis properties with an improved Jiles–Atherton (J-A) model through a unified set of parameters.

First, the waveform scaling parameters β, λ_k and λ_c are used to improve the calculation accuracy of hysteresis loops at low magnetic flux density. Second, the Riemann–Liouville (R-L) type fractional derivatives technique is applied to modified static inverse J-A model to compute the dynamic magnetic field considering the skin effect in wideband frequency magnetization conditions.

The proposed model is identified and verified by modeling the hysteresis loops whose maximum magnetic flux densities vary from 0.3 to 1.4 T up to 800 Hz using B30P105 electrical steel. Compared with the conventional J-A model, the global simulation ability of the proposed dynamic model is much improved.

Accurate modeling of the hysteresis properties of electrical steels is essential for analyzing the loss behavior of electrical equipment in finite element analysis (FEA). Nevertheless, the existing inverse Jiles–Atherton (J-A) model can only guarantee the simulation accuracy with higher magnetic flux densities, which cannot guarantee the analysis requirements of considering both low magnetic flux density and high magnetic flux density in FEA. This paper modifies the dynamic J-A model by introducing waveform scaling parameters and the R-L fractional derivative to improve the hysteresis loops’ simulation accuracy from low to high magnetic flux densities with the same set of parameters in a wide frequency range.

Widely used in micro‐position devices and vibration control, the piezoelectric actuator exhibits strong hysteresis effects, which can cause inaccuracy and oscillations, even lead to instability. If the hysteretic effects can be predicted, a controller can be designed to correct for these effects. This paper aims to present a neural network hysteresis model with an improved Preisach model to predict the output of piezoelectric actuator.

The improved Preisach model is given: A wiping‐out memory sequence is defined that is along a single axis only and at the same time the ascending and the descending extreme points are separated. The extended area variable is calculated according to wiping‐out memory sequence. The relationship between the two inputs (the extended area variable and variable rate of input signal) and the hysteresis output is implemented with a neural network to approximate the hysteresis model for the piezoelectric actuators.

Some experiments are carried out with a piezoelectric ceramic (PST150/7/40 VS12) and the results show the neural network hysteresis model can reliably predict the hysteretic behaviours in piezoelectric actuators.

The improved Preisach model is a simple model that is implemented by a neural network to reliably predict the hysteretic output in piezoelectric actuators.