Parameter Estimation for the Jiles–Atherton Model in Weak Fields

Vijn, Aad R. P. J.; Baas, Olivier; Lepelaars, E.S.A.M.

doi:10.1109/tmag.2020.2971435

Cited by 10 publications

(2 citation statements)

References 39 publications

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“…Modelling B m using the J-A hysteresis model [27,28], we can obtain the form of the field composition as shown in figure 4. As shown in figure 4(a), the simulation results illustrate the temporal variations of the B c , B m and B r .…”

Section: Field Compositionmentioning

confidence: 99%

Dynamic stabilisation of magnetic fields measured inside a magnetically shielded room using an external coil system ^*

Zhao,

Tian,

Sun

et al. 2024

J. Phys. D: Appl. Phys.

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Magnetoencephalography (MEG) system based on optically pumped magnetometers (OPMs) requires a magnetically shielded room (MSR) to establish a stable near-zero field environment. Affected by external environmental electromagnetic interference, the magnetic noise in the MSR will become very severe. In order to overcome this problem, this paper proposes a method for dynamic stabilisation of magnetic fields measured inside a MSR using an external coil system. Firstly, the field form of the external compensation coil was analyzed by taking the AC characteristics of the material into consideration. Then, the linear characteristic of the control system is studied and a high performance magnetic noise suppression controller is designed based on the environment noise characteristics. Finally, simulation and experimental are carried out through a self-developed 1250mm×1250mm×2100mm MSR, which indicates that the proposed method can effectively suppress dynamic magnetic fluctuation and noise.

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Section: Field Compositionmentioning

confidence: 99%

Dynamic stabilisation of magnetic fields measured inside a magnetically shielded room using an external coil system ^*

Zhao,

Tian,

Sun

et al. 2024

J. Phys. D: Appl. Phys.

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show abstract

“…The model is based on a clear set of parameters, compared to many other models, such as the Coleman-Hodgdon model or the Preisach model, that does not specifically state the curve shape or distribution function. Still, it is not obvious how the parameters should be found for the Jiles-Atherton model, and there are several studies that introduce methods for parameter estimation [34,[129][130][131][132][133][134][135]. There are methods on how to find the parameters in the model.…”

Section: Jiles-atherton Modelmentioning

confidence: 99%

Review of Hysteresis Models for Magnetic Materials

Mörée

Leijon

2023

Energies

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There are several models for magnetic hysteresis. Their key purposes are to model magnetization curves with a history dependence to achieve hysteresis cycles without a frequency dependence. There are different approaches to handling history dependence. The two main categories are Duhem-type models and Preisach-type models. Duhem models handle it via a simple directional dependence on the flux rate, without a proper memory. While the Preisach type model handles it via memory of the point where the direction of the flux rate is changed. The most common Duhem model is the phenomenological Jiles–Atherton model, with examples of other models including the Coleman–Hodgdon model and the Tellinen model. Examples of Preisach type models are the classical Preisach model and the Prandtl–Ishlinskii model, although there are also many other models with adoptions of a similar history dependence. Hysteresis is by definition rate-independent, and thereby not dependent on the speed of the alternating flux density. An additional rate dependence is still important and often included in many dynamic hysteresis models. The Chua model is common for modeling non-linear dynamic magnetization curves; however, it does not define classical hysteresis. Other similar adoptions also exist that combine hysteresis modeling with eddy current modeling, similar to how frequency dependence is included in core loss modeling. Most models are made for scalar values of alternating fields, but there are also several models with vector generalizations that also consider three-dimensional directions.

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A new artificial intelligence based supervision method for low-impedance REF relays

Ebadi

Hosseini

Abdoos

2021

Electric Power Systems Research

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Parameter Estimation for the Jiles–Atherton Model in Weak Fields

Cited by 10 publications

References 39 publications

Dynamic stabilisation of magnetic fields measured inside a magnetically shielded room using an external coil system ^*

Dynamic stabilisation of magnetic fields measured inside a magnetically shielded room using an external coil system ^*

Review of Hysteresis Models for Magnetic Materials

A new artificial intelligence based supervision method for low-impedance REF relays

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Parameter Estimation for the Jiles–Atherton Model in Weak Fields

Cited by 10 publications

References 39 publications

Dynamic stabilisation of magnetic fields measured inside a magnetically shielded room using an external coil system *

Dynamic stabilisation of magnetic fields measured inside a magnetically shielded room using an external coil system *

Review of Hysteresis Models for Magnetic Materials

A new artificial intelligence based supervision method for low-impedance REF relays

Contact Info

Product

Resources

About

Dynamic stabilisation of magnetic fields measured inside a magnetically shielded room using an external coil system ^*

Dynamic stabilisation of magnetic fields measured inside a magnetically shielded room using an external coil system ^*