Artificial Neural Network (ANN) Based Fast and Accurate Inductor Modeling and Design

Artificial Neural Network (ANN) Based Fast and Accurate Inductor Modeling and Design

This paper analyzes the potential of Artificial Neural Networks (ANNs) for the modeling and optimization of magnetic components and, specifically, inductors. After reviewing the basic properties of ANNs, several potential modeling and design workflows are presented. A hybrid method, which combines t...

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Bibliographic Details
Main Authors: Thomas Guillod, Panteleimon Papamanolis, Johann W. Kolar
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Open Journal of Power Electronics
Subjects:
Power converters
artificial neural networks
finite element analysis
inductors
machine learning
magnetic devices
Online Access:https://ieeexplore.ieee.org/document/9152082/
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Summary:This paper analyzes the potential of Artificial Neural Networks (ANNs) for the modeling and optimization of magnetic components and, specifically, inductors. After reviewing the basic properties of ANNs, several potential modeling and design workflows are presented. A hybrid method, which combines the accuracy of 3D Finite Element Method (FEM) and the low computational cost of ANNs, is selected and implemented. All relevant effects are considered (3D magnetic and thermal field patterns, detailed core loss data, winding proximity losses, coupled loss-thermal model, etc.) and the implemented model is extremely versatile (<inline-formula><tex-math notation="LaTeX">$30$</tex-math></inline-formula> input and <inline-formula><tex-math notation="LaTeX">$40$</tex-math></inline-formula> output variables). The proposed ANN-based model can compute <inline-formula><tex-math notation="LaTeX">$50^{\prime}000$</tex-math></inline-formula> designs per second with less than <inline-formula><tex-math notation="LaTeX">$3 \%$</tex-math></inline-formula> deviation with respect to 3D FEM simulations. Finally, the inductor of a <inline-formula><tex-math notation="LaTeX">$2$</tex-math></inline-formula>&#x2009;kW DC-DC buck converter is optimized with the ANN-based workflow. From the Pareto fronts, a design is selected, measured, and successfully compared with the results obtained with the ANNs. The implementation (source code and data) of the proposed workflow is available under an open-source license.
ISSN:2644-1314

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