학술논문
A Data-Driven Inductor Modeling Technique Using Parametric Circuit Simulation and Deep Learning
Document Type
Periodical
Author
Source
IEEE Transactions on Magnetics IEEE Trans. Magn. Magnetics, IEEE Transactions on. 59(11):1-5 Nov, 2023
Subject
Language
ISSN
0018-9464
1941-0069
1941-0069
Abstract
Optimization of magnetic components design, such as power inductors and transformers, is most needed to improve the performance of future power electronics. However, power electronic designers face the problem of not having sufficient magnetic component models available for their designs. In this article, we propose a method to construct a unique nonlinear magnetic component model using parametric circuit simulation and deep learning (DL).