Vector Hysteresis Model Associated With FEM in a Self-Excited Induction Generator Modeling

Padilha, Juliano Bitencourt; Kuo‐Peng, Patrick; Sadowski, N.; Batistela, Nelson Jhoe

doi:10.1109/tmag.2015.2483201

Cited by 12 publications

(3 citation statements)

References 13 publications

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“…Publication III proposes a method to include the Jiles-Atherton magnetic hysteresis model in the finite element simulations considered in Publications I-II. We found the methods introduced in earlier works [12,23] to be unstable if applied directly to the electrical machine simulations. Our proposed method solves the Jiles-Atherton material equation adaptively both in time and space, and numerical results illustrate stability and computational feasibility of the method.…”

Section: Heat Equationmentioning

confidence: 91%

Stable Adaptive Method to Solve FEM Coupled With Jiles–Atherton Hysteresis Model

et al. 2018

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Section: Heat Equationmentioning

confidence: 91%

Stable Adaptive Method to Solve FEM Coupled With Jiles–Atherton Hysteresis Model

et al. 2018

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“…In addition, most of EMSs related to energy conversion are in this category. For instance, the mechanical source or outcome are respectively in electric generators [14,15] or motors [16,17].…”

Section: Causative Couplingsmentioning

confidence: 99%

Coupled Models in Electromagnetic and Energy Conversion Systems from Smart Theories Paradigm to That of Complex Events: A Review

Razek

2022

Applied Sciences

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In this article, we evaluate the modeling of a real operation of a real system using the corresponding adequate theory. We show that the smart theories often used do not directly correspond to reality because these theories have been established in idealized frameworks. The need to adapt such frames to real landscape situations necessitates modifying the models used. This can be achieved by taking into account the different existing physical phenomena, which are normally overlooked in smart idealized models, in a revised coupled model. This contribution aims to analyze and illustrate the relationship between smart theories and coupled realistic models through a literature review. The strategy for constructing such models is discussed and highlighted. The understanding of this approach is illustrated by an application to the case of electromagnetic and energy conversion systems. In these systems, intelligent energy management, conversion and control involve the use of an accurate realistic coupled model in system design, optimization and control. It is a question of coupling and solving equations representing these systems by taking into account the real phenomena involved, which are electrical, magnetic, mechanical, thermal and material. The obvious advantage of using such realistic models in computer-aided design and optimization tools is illustrated. Moreover, the interest of using such models in the supervision of systems is assessed. These demonstrations are supported by a review of examples of work carried out in the field.

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“…Compared to more complex mathematical models, such as the modified Preisach-type models (Mayergoyz, 1991), the JA model is usually computationally cheaper (Benabou et al, 2003). Also, a vector extension has rendered the JA model suitable for magnetic field simulations using the finite element method (Bergqvist, 1996;Sadowski et al, 2002;Gyselinck et al, 2004;Padilha et al, 2016). It has also influenced the development of other hysteresis models, such as the energy-based model (Henrotte et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

A constraint-based optimization technique for estimating physical parameters of Jiles – Atherton hysteresis model

Upadhaya

Rasilo²,

Perkkiö

et al. 2020

COMPEL

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Purpose Improperly fitted parameters for the Jiles–Atherton (JA) hysteresis model can lead to non-physical hysteresis loops when ferromagnetic materials are simulated. This can be remedied by including a proper physical constraint in the parameter-fitting optimization algorithm. This paper aims to implement the constraint in the meta-heuristic simulated annealing (SA) optimization and Nelder–Mead simplex (NMS) algorithms to find JA model parameters that yield a physical hysteresis loop. The quasi-static B(H)-characteristics of a non-oriented (NO) silicon steel sheet are simulated, using existing measurements from a single sheet tester. Hysteresis loops received from the JA model under modified logistic function and piecewise cubic spline fitted to the average M(H) curve are compared against the measured minor and major hysteresis loops. Design/methodology/approach A physical constraint takes into account the anhysteretic susceptibility at the origin. This helps in the optimization decision-making, whether to accept or reject randomly generated parameters at a given iteration step. A combination of global and local heuristic optimization methods is used to determine the parameters of the JA hysteresis model. First, the SA method is applied and after that the NMS method is used in the process. Findings The implementation of a physical constraint improves the robustness of the parameter fitting and leads to more physical hysteresis loops. Modeling the anhysteretic magnetization by a spline fitted to the average of a measured major hysteresis loop provides a significantly better fit with the data than using analytical functions for the purpose. The results show that a modified logistic function can be considered a suitable anhysteretic (analytical) function for the NO silicon steel used in this paper. At high magnitude excitations, the average M(H) curve yields the proper fitting with the measured hysteresis loop. However, the parameters valid for the major hysteresis loop do not produce proper fitting for minor hysteresis loops. Originality/value The physical constraint is added in the SA and NMS optimization algorithms. The optimization algorithms are taken from the GNU Scientific Library, which is available from the GNU project. The methods described in this paper can be applied to estimate the physical parameters of the JA hysteresis model, particularly for the unidirectional alternating B(H) characteristics of NO silicon steel.

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Vector Hysteresis Model Associated With FEM in a Self-Excited Induction Generator Modeling

Cited by 12 publications

References 13 publications

Stable Adaptive Method to Solve FEM Coupled With Jiles–Atherton Hysteresis Model

Stable Adaptive Method to Solve FEM Coupled With Jiles–Atherton Hysteresis Model

Coupled Models in Electromagnetic and Energy Conversion Systems from Smart Theories Paradigm to That of Complex Events: A Review

A constraint-based optimization technique for estimating physical parameters of Jiles – Atherton hysteresis model

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