Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics-Reference-Cited by-同舟云学术

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Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics

Published:2023-11 Issue:11 Volume:38 Page:14292-14316
ISSN:0885-8993
Container-title:IEEE Transactions on Power Electronics
language:
Short-container-title:IEEE Trans. Power Electron.

Author:

Serrano Diego¹^ORCID,Li Haoran¹^ORCID,Wang Shukai¹^ORCID,Guillod Thomas²^ORCID,Luo Min³^ORCID,Bansal Vineet¹,Jha Niraj K.¹^ORCID,Chen Yuxin⁴^ORCID,Sullivan Charles R.²^ORCID,Chen Minjie¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering and the Andlinger Center for Energy and the Environment, Princeton University, Princeton, NJ, USA

2. Dartmouth College, Hanover, NH, USA

3. BASiC Semi, Zurich, Switzerland

4. Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, PA, USA

Funder

Advanced Research Projects Agency—Energy

Schmidt DataX Fund at Princeton University

Schmidt Futures Foundation

Dartmouth College

Plexim GmbH

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/63/10261409/10169101.pdf?arnumber=10169101

Reference77 articles.

1. Analysis and Experimental Investigation of High-Frequency Magnetic Flux Distribution in Mn-Zn Ferrite Cores

2. Accurate prediction of ferrite core loss with nonsinusoidal waveforms using only Steinmetz parameters

3. Summary of losses in magnetic materials

4. A Core Loss Calculation Method for DC/DC Power Converters Based on Sinusoidal Losses

5. Artificial Neural Network Aided Loss Maps for Inductors and Transformers

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1. Physics-Inspired Multimodal Feature Fusion Cascaded Networks for Data-Driven Magnetic Core Loss Modeling;IEEE Transactions on Power Electronics;2024-09

2. Conditional Generative Adversarial Network Aided Iron Loss Prediction for High-Frequency Magnetic Components;IEEE Transactions on Power Electronics;2024-08

3. Artificial Intelligence Applications in High-Frequency Magnetic Components Design for Power Electronics Systems: An Overview;IEEE Transactions on Power Electronics;2024-07

4. A Hybrid Data-Driven Approach in Magnetic Core Loss Modeling for Power Electronics Applications;2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON);2024-06-25

5. Temperature Control for Automated High Frequency Core Loss Testing;2024 IEEE Workshop on Control and Modeling for Power Electronics (COMPEL);2024-06-24

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