Calculation of mutual inductance from magnetic vector potential for wireless power transfer applications

Beams, David M.; Annam, Sravan G.

doi:10.1109/ssst.2012.6195124

Cited by 10 publications

(4 citation statements)

References 3 publications

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“…They can be calculated by integrating the resulting vector potentials A 1 or A 2 over the corresponding coil cross-section, cf. [22], [23]. The coupling k between two coils is given via the symmetric mutual inductance M := L 12 = L 21 as k = M √ L11 L22 .…”

Section: B Physical Modelmentioning

confidence: 99%

Physics-Informed Neural Networks for Magnetostatic Problems on Axisymmetric Transformer Geometries

Brendel,

Medvedev,

Rosskopf

2024

IEEE J. Emerg. Sel. Top. Ind. Electron.

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Physics-Informed Neural Networks (PINNs) have shown their potential for solving direct problems in many engineering domains including electromagnetics. We employ fully connected and convolutional PINNs in order to predict the magnetic vector potential and resulting inductances and coupling for axisymmetric transformer geometries commonly used in inductive power systems. Both approaches are compared quantitatively and validated against reference solutions obtained from a numerical solver. Fully connected PINNs tend to train faster and more accurate for single geometries, whereas convolutional PINNs show an outperformance in terms of their generalization capability and are able to predict inductances and coupling for a wide range of transformer geometries accurately in a matter of milliseconds. The combination of high accuracy and fast inference paves the way for PINN-based topology optimization in the field of power electronics.

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Section: B Physical Modelmentioning

confidence: 99%

Physics-Informed Neural Networks for Magnetostatic Problems on Axisymmetric Transformer Geometries

Brendel,

Medvedev,

Rosskopf

2024

IEEE J. Emerg. Sel. Top. Ind. Electron.

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“…Basically, the iterative numerical scheme is adjusting the number of sub-segments to be integrated as well as the order of the numerical quadrature until reaching the required numerical precision [25]. The inductance component L ik (e.g., L 01,02 ) is computed using the following formula [31]…”

Section: Electromagnetic Modelmentioning

confidence: 99%

A Rectangular End-Winding Model for Enhanced Circulating Current Prediction in AC Machines

Maurer

Nøland

2021

IEEE Trans. Energy Convers.

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The end-winding region is usually overlooked in the roebel design of large AC machines. However, saturated stator slots cause overhang parts to impact the circulating currents significantly. In fact, a precise knowledge of the winding overhang strand inductances is crucial when optimising the transpositions of large roebel bars, especially in the case of under-roebeling (having less than 360-transposition in the active part) where the goal is to compensate the winding overhang parasitic field with the slot-parasitic field. In this paper, a rectangular inductance calculation model (R-ICM) is proposed. It results in a circuital lumped-element model (LEM) that takes the strand dimensions, the bar bending, as well as small-scale effects into account. Moreover, the work describes how to model finite current-carrying rectangular segments (straight and arced) from first principles. Finally, the methodology was demonstrated for two prototype specimens with different bending-shapes corresponding to the fundamental elements on a stator bar in the winding overhang of large electrical machines.

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“…In references [7], [8], [9], [10], [11], and [12], the high permeability material such as ferrite attached in the coil is represented by a image coil, which exists in a plane symmetrical to the ferrite boundary plane. In reality, however, the ferrite shield is of finite size and thickness, which introduces errors in the design values.…”

Section: Introductionmentioning

confidence: 99%

Theorizing a Simple Ferrite Cored Coil Using Image Coils in Wireless Power Transfer

2023

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The technology of wireless power transfer, which wirelessly transmits power to any device, has been widely studied. Wireless power transfer using magnetic field resonance involves the use of coils and a material called ferrite, which improves the transmission characteristics. Until now, electromagnetic field analysis has been used to design ferrite cored coils, but electromagnetic field analysis requires a long analysis time and a computer with a large amount of memory. In this study, the ferrite shield and coil are represented as a image coil under the condition that the positional relationship between the ferrite shield and the coil does not change. This enabled the derivation of mutual inductance only by numerical analysis, which had been difficult up to now. In addition, experiments have shown that when the coil size does not change, mutual inductance can be derived only by numerical analysis using arbitrary coil parameters, regardless of the number of turns and pitch. The average error in deriving the design value was 5.2%. This facilitated the design of ferrite cored coils at 85 kHz and contributed greatly to the design of the system.INDEX TERMS Wireless power transfer, ferrite core, coil design, numerical analysis, inductive wireless power transfer, image method.

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Calculation of mutual inductance from magnetic vector potential for wireless power transfer applications

Cited by 10 publications

References 3 publications

Physics-Informed Neural Networks for Magnetostatic Problems on Axisymmetric Transformer Geometries

Physics-Informed Neural Networks for Magnetostatic Problems on Axisymmetric Transformer Geometries

A Rectangular End-Winding Model for Enhanced Circulating Current Prediction in AC Machines

Theorizing a Simple Ferrite Cored Coil Using Image Coils in Wireless Power Transfer

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