Influence of Steel Manufacturing on J-A Model Parameters and Magnetic Properties

Vaseghi, Babak; Rahman, S. A.; Knight, A.M.

doi:10.1109/tmag.2013.2242054

Cited by 13 publications

(3 citation statements)

References 14 publications

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“…They show that the performance of the TGU method is comparable to DE, PSO and GA. In contrast, Kis and Ivanyi (2004), Vaseghi et al (2013a), Vaseghi et al (2013b) and Rasilo et al (2015) use computationally efficient methods, such as nonlinear least-square and pattern-search.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…A measured hysteresis loop of a non-oriented (NO) silicon steel sheet includes a sharp rising curve until the knee region and it flattens quickly in the saturation region (Vaseghi et al, 2013a). This typical magnetic behavior presents difficulties when fitting the curve with a (combination of) transcendental function(s) (Kokornaczyk and Gutowski, 2015;Steentjes et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Parameter Estimationmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…T HE magnetic characteristics of the core laminations of electrical machines are typically deteriorated as a result of the manufacturing process [1], especially punching [2]. Since simulation models are typically identified with measurements from separate lamination samples, modeling results may differ significantly from measurements done for a processed and assembled machine.…”

Section: Introductionmentioning

confidence: 99%

Identification of Synchronous Machine Magnetization Characteristics From Calorimetric Core-Loss and No-Load Curve Measurements

et al. 2015

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The magnetic material characteristics of a wound-field synchronous machine are identified based on global calorimetric coreloss and no-load curve measurements. This is accomplished by solving a coupled experimental-numerical electromagnetic inverse problem, formulated to minimize the difference between a finite-element (FE) simulation-based Kriging surrogate model and the measurement results. The core-loss estimation in the FE model is based on combining a dynamic iron-loss model and a static vector Jiles-Atherton hysteresis model, the parameters for which are obtained by solving the inverse problem. The results show that reasonable hysteresis loops can be produced for a grid-supplied machine, while for an inverter-supplied machine the limitations in the FE and iron-loss models seemingly exaggerate the area of the loop. In addition, the effect of the measurement uncertainty on the inverse problem is quantitatively estimated.

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A method for determining the local magnetic induction near the cut edge of the ferromagnetic strip

Gmyrek

2016

Journal of Magnetism and Magnetic Materials

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Influence of Steel Manufacturing on J-A Model Parameters and Magnetic Properties

Cited by 13 publications

References 14 publications

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

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

Identification of Synchronous Machine Magnetization Characteristics From Calorimetric Core-Loss and No-Load Curve Measurements

A method for determining the local magnetic induction near the cut edge of the ferromagnetic strip

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