Iron Loss Extrapolation Predictions for High-Frequency Magnetic Components Using Denoising Diffusion Models

This article introduces denoising diffusion probabilistic models to enhance core loss predictions in high-frequency magnetic components. Conventional methods, such as the Steinmetz equation, often fail to accurately capture the nonlinear dynamics and complex waveforms characteristic of high-frequency magnetic core losses. Previous approaches using multilayer perceptron, transfer learning, and generative adversarial networks have encountered issues, such as data boundaries and noise, adversely affecting prediction accuracy. Denoising diffusion probabilistic models overcome these challenges by improving both interpolation and extrapolation capabilities, achieving a minimum average relative error of 0.99% across all datasets provided by the MagNet challenge. Furthermore, denoising diffusion probabilistic models, first introduced into power electronics, support effective data augmentation, validated through the generation of high-quality core loss data for Nanocrystalline ring cores in three different sizes (R16, R25, R50). This robust modeling framework not only reduces prediction errors but also enhances dataset comprehensiveness, thereby facilitating the design and optimization of next-generation power electronic components.