Modeling of Core Loss Based on Machine Learning and Deep Learning

TL;DR

This paper introduces a novel Mix Neural Network model based on CNN-FCNN architecture for accurately predicting magnetic core loss across various materials and conditions, outperforming traditional empirical models.

Contribution

It proposes a unified deep learning model capable of predicting core loss for multiple materials and conditions, simplifying the modeling process and improving accuracy.

Findings

MNN predicts for four materials with high accuracy

Deep learning models outperform traditional empirical equations

Hybrid MNN-XGBoost further improves prediction accuracy

Abstract

This article proposes a Mix Neural Network (MNN) based on CNN-FCNN for predicting magnetic loss of different materials. In traditional magnetic core loss models, empirical equations usually need to be regressed under the same external conditions. When the magnetic core material is different, it needs to be classified and discussed. If external factors increase, multiple models need to be proposed for classification and discussion, making the modeling process extremely cumbersome. And traditional empirical equations still has the problem of low accuracy, although various correction equations have been introduced later, the accuracy has always been unsatisfactory. By introducing machine learning and deep learning, it is possible to simultaneously solve prediction problems with low accuracy of empirical equations and complex conditions. Based on the MagNet database, through the training of the newly proposed MNN, it is found that a single model is sufficient to make predictions for at least four different materials under varying temperatures, frequencies, and waveforms, with accuracy far exceeding that of traditional models. At the same time, we also used three other machine learning and deep learning models (Random Forest, XGBoost, MLP-LSTM) for training, all of which had much higher accuracy than traditional models. On the basis of the predicted results, a hybrid model combining MNN and XGBoost was proposed, which predicted through weighting and found that the accuracy could continue to improve. This provides a solution for modeling magnetic core loss under different materials and operating modes.