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Machine Learning Framework for Modeling Power Magnetic Material Characteristics
  • Minjie Chen
Minjie Chen
Princeton University, Princeton University, Princeton University

Corresponding Author:[email protected]

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This paper applies machine learning to modeling power magnetics. We first introduce an open-source database - MagNet - which hosts a large amount of experimentally measured excitation data for many materials across a variety of operating conditions. The processes for data acquisition and data quality control are explained. We then demonstrate a few neural network-based power magnetics modeling tools for modeling core losses and B–H loops. Machine learning allows the many factors that may influence the magnetic characteristics being modeled in a unified framework, and provides insights to quantify the complexity of magnetic characteristics and reduce the size of the measurement data required to build a precise model. Neural network models are found effective in compressing the measurement data and predicting the behaviors of magnetic materials such as the core loss and the B–H loop. The behaviors of a typical power magnetic material (e.g., TDK N87) across a wide range of operating conditions (e.g., temperature, waveform, dc-bias) can be well described by a small-scale neural network (200 KB) which is 10,000 times smaller than the raw measured time-series data (2 GB), paving the way toward using neural networks as an interactive datasheet to assist magnetics design.