A Data-Driven Inductor Modeling Technique Using Parametric Circuit Simulation and Deep Learning

A Data-Driven Inductor Modeling Technique Using Parametric Circuit Simulation and Deep Learning

Optimization of magnetic components design, such as power inductors and transformers, is most needed to improve the performance of future power electronics. However, power electronics designers face the problem of not having sufficient magnetic component models available for their designs. In this p...

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Veröffentlicht in:IEEE transactions on magnetics 2023-11, Vol.59 (11), p.1-1
Hauptverfasser: Motomatsu, Takehiro, Koga, Takahiro, Shigei, Noritaka, Yamaguchi, Masahiro, Itagaki, Atsushi, Ishizuka, Yoichi
Format: Artikel
Sprache:eng
Schlagworte:
Analytical models
Circuit design
Copper loss
Data models
Deep learning
Design optimization
Equivalent circuits
Inductors
Integrated circuit modeling
Magnetic circuits
Magnetic resonance imaging
Magnetism
power inductor
Vorperian loss model
waveform image
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Zusammenfassung:Optimization of magnetic components design, such as power inductors and transformers, is most needed to improve the performance of future power electronics. However, power electronics designers face the problem of not having sufficient magnetic component models available for their designs. In this paper, we propose a method to construct a unique nonlinear magnetic component model using parametric circuit simulation and deep learning.
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2023.3299110