Dimitre Makaveev

Calculation of no load losses in an induction motor using an inverse vector Preisach model and an eddy current loss model

This paper deals with the direct inclusion of vector hysteresis and eddy current losses in a 2D finite element (FE) time stepping analysis. It is shown that a vector Preisach model can be inverted in an efficient way by means of the Newton-Raphson method and that it thus can easily be included In the Newton-Raphson iterations of the FE equations. The eddy current losses are accounted for by considering an additional conductivity matrix in the FE equations. The method is applied to the no load simulation of an induction motor. The calculation results are discussed and compared to measurements.

Dependence of power losses on tensile stress for Fe–Si nonoriented steel up to destruction

The study of the influence of mechanical stress on magnetic properties of laminated steel is the subject of recent research. In this article the separation of losses is performed and the dependence of total, hysteresis, and excess losses on tensile stress up to destruction is investigated. With increasing of tensile stress, the total and hysteresis losses are first decreasing and then increasing at higher stress. The increase of total losses was found mainly due to the hysteresis losses while the classical and excess components influence the total power losses to a minor extent. For extensive deformation the increase of losses becomes dramatically high.

Modeling of quasistatic magnetic hysteresis with feed-forward neural networks

A modeling technique for rate-independent (quasistatic) scalar magnetic hysteresis is presented, using neural networks. Based on the theory of dynamic systems and the wiping-out and congruency properties of the classical scalar Preisach hysteresis model, the choice of a feed-forward neural network model is motivated. The neural network input parameters at each time step are the corresponding magnetic field strength and memory state, thereby assuring accurate prediction of the change of magnetic induction. For rate-independent hysteresis, the current memory state can be determined by the last extreme magnetic field strength and induction values, kept in memory. The choice of a network training set is motivated and the performance of the network is illustrated for a test set not used during training. Very accurate prediction of both major and minor hysteresis loops is observed, proving that the neural network technique is suitable for hysteresis modeling.

Accurate field strength measurement in rotational single sheet testers

The accuracy of the field strength measurement in rotational single sheet testers (RSST) has been investigated by means of both finite element calculations and measurements on the built set-up. Shielding laminations should be used above and underneath the actual sample in order to obtain a two-dimensional and homogeneous magnetization and to measure the field strength accurately.

Dynamic hysteresis modelling using feed-forward neural networks

A computational model for dynamic hysteresis in laminated SiFe alloys is proposed, based on feed-forward neural networks. The model employs the loss-separation property of ferromagnetic materials and combines a rate-independent hysteresis model with a correction technique for dynamic effects at each time point. The model yields accurate prediction of BH loops for arbitrary waveforms and frequencies, as they occur in electrical motors.

Neural network based approach to dynamic hysteresis for circular and elliptical magnetization in electrical steel sheet

Summary form only given. Feed-forward neural networks have been successfully applied to model quasi-static and dynamic unidirectional magnetization, as well as quasi-static vector magnetization for circular and elliptical magnetization patterns in electrical steel sheets. The magnetic state of the material is thereby the input of the neural network. In the case of quasi-static circular and elliptical magnetization, the magnetic state is determined by the value of the magnetic induction vector B(t) (amplitude and phase), together with the maximum amplitude and axis ratio of the considered magnetization pattern. Dynamic hysteresis is treated based on the loss separation property of magnetic materials. The dynamic field can be determined the same way as in the case of unidirectional excitation, with a neural network. Consequently, only the unidirectional sinusoidal loops for saturation along the x- and the y- axis for different frequencies need to be measured. This approach reduces the required amount of measurement data substantially, while retaining good accuracy for the particular case of low and moderate induction levels. Standard neural network techniques are used. The numerical investigation of the accuracy of the proposed approach is described.

Controlled circular magnetization of electrical steel in Rotational Single Sheet Testers

A new waveform control algorithm for Rotational Single Sheet Testers (RSST) is presented. The new algorithm achieves circular magnetization for high induction levels even in samples of grain-oriented silicon steel sheets. The algorithm is based on the circuit model of the system and takes the coupling between the two phases of the RSST into account. Identification of the parameters of the measurement setup with the Least Squares (LS) method avoids preliminary measurements on the sample and the setup. The performance of the algorithm is illustrated by measurements on a grain-oriented sample. The algorithm is universally applicable and allows fully automated measurements.

Modelling of electromagnetic losses in asynchronous machines

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.

Combined Preisach–Mayergoyz-neural-network vector hysteresis model for electrical steel sheets

Three versions of a vector hysteresis model for electrical steel sheets are presented, based on the function approximation capabilities of feed-forward neural networks and the memory mechanism of vector hysteresis proposed by Mayergoyz. The first model handles arbitrary vector magnetization patterns, but requires a very extended data set for the training of the neural network. The second model is suitable for convex induction loci and allows a reduction of the required training set. The third model handles the features of the considered magnetization pattern in an alternative way and relaxes the convexity requirement. The choice of the specific model, its parameters, and the network training set depends on the types of magnetization patterns concerned. Arbitrary high accuracy can be reached by extending the complexity of the model and/or the size of the training set. Experimental results for the third model are presented and show the good accuracy of the approach. Standard neural network algorithms are used.

Preisach modeling of magnetization and magnetostriction processes in laminated SiFe alloys

In this article, magnetization loops under mechanical stress and magnetostriction loops under quasistatic magnetic excitation conditions are discussed. In both cases, the hysteresis loops are modeled using the Preisach theory. The identification procedure of the material parameters is described. The article discusses first the shape of the Preisach distribution function for the study of magnetostriction loops. Next, a Preisach model is proposed for the description of magnetization loops under mechanical stress starting from the magnetization loop obtained without applying mechanical stress. A setup has been constructed for the measurement of magnetization loops under compressive or tensile stress. Also, a measuring system based on a single sheet tester and on optical displacement measurement techniques is used to establish the magnetostrictive behavior of laminated SiFe alloys. It is shown that a good correspondence between the calculated and measured magnetization and magnetostriction loops is obtained.