MagLearn – Data-driven Machine Learning Framework with Transfer and Few-shot Training for Modeling Magnetic Core Loss

In response to the MagNet Challenge 2023, this paper describes the solution developed by the University of Bristol team, awarded the 3rd Place Outstanding Performance among 24 competing teams worldwide. The core loss of magnetic components has been a challenge for engineers to model due to the lack of full physics models. Classic Steinmetz-Equation-based approaches show significant limitations under power electronics excitations. Data-driven approaches have emerged in recent years as a new solution to this problem as an active research area. Based on the datasets supplied by PowerLab Princeton, this work employs a machine learning framework to predict the core loss of magnetic components from a range of flux density waveforms, e.g. sinusoidal, rectangular, trapezoidal, as the input. The proposed approach builds on an LSTM neural network to extract features from the input B waveforms and predict the power loss value. Designed for the small and imbalanced datasets supplied in the competition, a machine learning pipeline is proposed in this work featuring transfer learning and few-shot training, which is realized through data augmentation and alignment. As a modification to decouple the output from the phase shift of the input waveform, a random shift/flip algorithm is applied in both pre- and post-processing blocks. The performance of the proposed approach is validated through the experimentally measured testing sets, demonstrating a high prediction accuracy.