Modified Jiles–Atherton model and parameters identification using false position method

Hamimid, M.; Féliachi, Mouloud; Mimoune, S.M.

doi:10.1016/j.physb.2010.01.078

Cited by 33 publications

(12 citation statements)

References 9 publications

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“…The Modified Inverse Jiles-Atherton model (MIJA) considers the magnetic flux density B as an independent variable [8]- [9], is given below by equation (1) with He and Man are, respectively, the effective field and the anhysteretic magnetization, the directional parameter d is taking the value +1 if dB/dt > 0 and -1 if dB/dt < 0. a, a, c, k and the saturation magnetization Ms are the five model parameters which have to be determined from measured major hysteresis loops.…”

Section: Quasi-static Inverse Jiles -Atherton Modelmentioning

confidence: 99%

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Accurate Hysteresis Loops Calculation Under the Frequency Effect Using the Inverse Jiles-Atherton Model

Belgasmi¹,

Hamimid

2020

AEM

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Dans ce présent travail, une tentative a été faite pour améliorer la forme des boucles d'hystérésis ainsi qu'un calcul précis des pertes de fer sous l'effet de fréquence. Le modèle inverse de Jiles-Atherton est étendu pour décrire l'aimantation du comportement des matériaux ferromagnétiques en régime dynamique. Une nouvelle formulation du champ magnétique efficace est utilisée qui consiste à modifier l'expression du champ excédentaire pour prendre correctement en compte l'effet des parois du domaine mobile. La nouvelle expression proposée du champ effectif permet une bonne représentation du comportement d'hystérésis magnétique vis-à-vis de l'augmentation de fréquence. Pour valider cette proposition, on compare des boucles d'hystérésis mesurées et modélisées pour différentes fréquences.

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Section: Quasi-static Inverse Jiles -Atherton Modelmentioning

confidence: 99%

“…The mean length of the magnetic core is 147 mm. Table I presents these parameters identified in quasi-static regime (10 Hz, this is the stable lower limit of our generator), by using the same procedure cited as in [8]. Figure 1 shows the measured and the modelled hysteresis loops.…”

Section: Quasi-static Inverse Jiles -Atherton Modelmentioning

confidence: 99%

Accurate Hysteresis Loops Calculation Under the Frequency Effect Using the Inverse Jiles-Atherton Model

Belgasmi¹,

Hamimid

2020

AEM

Self Cite

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“…In [14], random and deterministic searches were done to perform the identification. False position method is used in [15] as a new method of Power transformer model in railway applications based on bond graph and parameter identification Amirdehi S., Trajin B., Vidal P.-E., Vally J. and Colin D.…”

Section: Introductionmentioning

confidence: 99%

Power Transformer Model in Railway Applications Based on Bond Graph and Parameter Identification

Amirdehi

Trajin

Vidal

et al. 2020

IEEE Trans. Transp. Electrific.

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Didier Power transformer model in railway applications based on bond graph and parameter identification. (2020) IEEE Transactions on Transportation Electrification.Abstract-Validation and verification are the most important issues in railway applications due to cost and security reasons. Therefore, having a model of the system would be necessary in this case. Due to non-ideal test conditions in industrial applications, an accurate parameter identification process has to be defined. In this paper, bond graph method is used to model energy exchanges within components of a traction chain. More precisely, the nonlinear transformer model and its parameter identification is studied. In the case of non-ideal test conditions, the usual Jiles-Atherton parameter identification procedure can not be performed. Regarding state of the art, the Jiles-Atherton parameter identification is discussed. It is highlighted that an uncomplete hysteresis cycle, including extremum point and coercive field are mandatory for an accurate parameter identification. The proposed identification process is applied to a real application case. The obtained parameters are then inserted into the overall system model. The consecutive simulations are compared to experimental data obtained through traction chain test bench.

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“…Furthermore, except under controlled situations, the impressed magnetic field usually cannot be fully characterized, secondary effects, such as reverse magnetostriction [1], [2], introduce further randomness into a material's magnetic history, and magnetic materials are in general anisotropic and inhomogeneous. Even when suitable models are available for a particular material, the identification problem [3]- [9] of matching a material's physical properties to the model's parameters is a nontrivial task. Finally, assuming one has an adequate model with appropriate parameters identified, incorporating the model into a computational algorithm that can handle hysteresis effects, such as major and minor loops, arbitrary field reversals, and permanent magnetization, is a further difficulty that must be surmounted.…”

Section: Introductionmentioning

confidence: 99%

A Stepped Nonlinear Solver for Nonlinear Magnetic Materials With Hysteresis

et al. 2015

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A stepped nonlinear solution method based on differential susceptibility is presented for the quasi-magnetostatic analysis of nonlinear magnetic materials. The stepped solver is applicable to hysteretic materials and supports a permanent magnetization. A locally corrected Nyström discretization of the magnetostatic volume integral equation is used to implement the stepped solver. Characterization of the stepped solver is performed using various magnetic material models which support hysteresis. Solver accuracy is validated using Team Workshop Problem 13, analytic results of a magnetic sphere with hysteresis, and against measured data for a hollow steel pipe.

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Modified Jiles–Atherton model and parameters identification using false position method

Cited by 33 publications

References 9 publications

Accurate Hysteresis Loops Calculation Under the Frequency Effect Using the Inverse Jiles-Atherton Model

Accurate Hysteresis Loops Calculation Under the Frequency Effect Using the Inverse Jiles-Atherton Model

Power Transformer Model in Railway Applications Based on Bond Graph and Parameter Identification

A Stepped Nonlinear Solver for Nonlinear Magnetic Materials With Hysteresis

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