This paper summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic ma- terials. The MagNet Challenge has (1) advanced the state- of-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research com- munity; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advance- ments in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community.

MagNet Challenge for Data-Driven Power Magnetics Modeling / Chen, M., Li, H., Wang, S., Guillod, T., Serrano, D., Forster, N., Kirchgassner, W., Piepenbrock, T., Schweins, O., Wallscheid, O., Huang, Q., Li, Y., Dou, Y.u., Li, B.o., Li, S., Havugimana, E., Chacko, V.T., Radhakrishnan, S., Ranjram, M., Sauter, B., et al.. - In: IEEE OPEN JOURNAL OF POWER ELECTRONICS. - ISSN 2644-1314. - (2024), pp. 1-16. [10.1109/ojpel.2024.3469916]

MagNet Challenge for Data-Driven Power Magnetics Modeling

Solimene, Luigi;Ragusa, Carlo Stefano;
2024

Abstract

This paper summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic ma- terials. The MagNet Challenge has (1) advanced the state- of-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research com- munity; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advance- ments in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2993061