COMPEL 2020: A Machine Learning Framework for Magnetic Core Loss Modeling

Conference: COMPEL 2020
Presenter: Haoran Li
Abstract: This talk presents a two-stage machine learning framework - MagNet - for magnetic core loss modeling. The first stage of MagNet is a waveform transformation network, which generates 2-D images (tensors) and extracts both the frequency and time domain features from the magnetic excitation waveforms; the second stage of MagNet is a convolutional neural network (CNN), which is trained to recognize the patterns in the 2-D images and predict the core loss based on regression. MagNet is supported by a hardware-in-the-loop (HIL) data acquisition system, which can automatically generate a large amount of data to train the neural network models. Preliminary evaluations of MagNet are demonstrated and analyzed.