MagNetX: Foundation Neural Network Models for Simulating Power Magnetics in Transient

作者：Shukai Wang, Hyuk Jae Kwon, Haoran Li, Youssef Elasser, Gyeong-Gu Kang, Daniel H. Zhou, Davit Grigoryan, Minjie Chen · 年份：2025 · DOI：10.1109/apec48143.2025.10977420 · 被引用次数：6 · 研究领域：Electric Motor Design and Analysis、Sensorless Control of Electric Motors、Magnetic Properties and Applications

This paper introduces a foundation neural network framework for modeling power magnetics in transient, based on MagNetX1– a new extension of the MagNet database which includes extensive measurement data in transient. Provided with flux density B(t) and field intensity H(t) waveforms, the model uses partial memories and the next-state flux density excitation to predict the response of the field intensity in the next time step. The model is in time domain and is frequency independent. An example sequence-to-scalar LSTM neural network was designed, trained, and tested. This modeling framework can greatly enhance the modeling and design of power magnetics operating in transient condition, such as in PFCs and power amplifiers.