Abstract
This paper introduces an open-source database-MagNet-for data-driven magnetic core loss modeling. MagNet aims to support data-driven magnetics research by hosting a large amount of experimentally measured excitation waveform data for many materials across a variety of operating conditions. This database in its current state contains over 150,000 excitation waveforms for six ferrite materials-TDK{N27, N49, N87}, Ferroxcube{3C90, 3C94}, Fair-Rite{78}-in the 50 kHz to 500 kHz, 10 mT to 300 mT range for sinusoidal, triangle, and trapezoidal waveforms. This paper presents the purposes of building MagNet, introduces the data acquisition system and data format, discusses the data quality, and presents a few examples of using this database with data driven methods.
Original language | English (US) |
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Pages | 588-595 |
Number of pages | 8 |
DOIs | |
State | Published - 2022 |
Event | 37th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2022 - Houston, United States Duration: Mar 20 2022 → Mar 24 2022 |
Conference
Conference | 37th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2022 |
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Country/Territory | United States |
City | Houston |
Period | 3/20/22 → 3/24/22 |
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
Keywords
- core loss
- data-driven method
- machine learning
- neural network
- power magnetics