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 |
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| container_title | IEEE transactions on magnetics |
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| creator | Motomatsu, Takehiro Koga, Takahiro Shigei, Noritaka Yamaguchi, Masahiro Itagaki, Atsushi Ishizuka, Yoichi |
| description | 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. |
| doi_str_mv | 10.1109/TMAG.2023.3299110 |
| format | Article |
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| source | IEEE Electronic Library (IEL) |
| subjects | 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 |
| title | A Data-Driven Inductor Modeling Technique Using Parametric Circuit Simulation and Deep Learning |
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