Data-Driven Core Loss Prediction Model Research and Magnetic Component Optimization Design

Zhecheng Yin; Linpei Shou; Yushan Ye

doi:10.54097/qjs44618

Authors

Zhecheng Yin
Linpei Shou
Yushan Ye

DOI:

https://doi.org/10.54097/qjs44618

Keywords:

Core loss prediction, Magnetic component optimization, Data-driven, Steinmetz's equation, Model construction.

Abstract

Accurate core loss prediction is essential for the design of high-performance magnetic components for power electronics. In this study, a modified Steinmetz's equation incorporating a temperature factor is introduced to improve the accuracy of core loss prediction. By analyzing the independent and synergistic effects of various factors on core loss using multiple linear regression, partial correlation analysis, and Bayesian factor analysis, this study applies a genetic algorithm to find the conditions for minimizing core loss and constructed a model based on the Random Forest algorithm for predicting the core loss of different materials. In addition, considering that core loss is not the only thing that affects the performance of magnetic elements, this study considers both core loss and transmitted magnetic energy and uses particle swarm optimization (PSO) to determine the optimal conditions for magnetic elements. It is shown that the modified model and optimization method can more accurately predict and optimize the performance of magnetic elements, providing a comprehensive solution for power conversion systems.

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