综合智慧能源2025，Vol.47Issue(4)：73-84,12.DOI:10.3969/j.issn.2097-0706.2025.04.006
基于数据驱动方法的磁性元件损耗研究
Research on the loss of magnetic components based on a data-driven method
摘要
Abstract
To improve the efficiency of power converters,it is necessary to conduct a correlation analysis of the factors affecting the magnetic core loss of magnetic components in power converters.The core loss under the influence of specific factors can be predicted using regression methods.To improve the accuracy of core loss prediction,a data-driven method was adopted,employing a decision gradient boosting model and out-of-bag error variation to independently analyse the influencing factors.The K-means clustering method combined with the silhouette coefficient method was used to cluster the influencing factors and analyse the synergistic effects of factor combinations on core loss.Based on the importance analysis of these factors,the GhostNet neural network was used for prediction.A multi-objective genetic algorithm was used to explore the conditions under which magnetic components achieved maximum transmitted magnetic energy while minimizing core loss.Simulation results demonstrated that the proposed GhostNet-based core loss prediction method achieved excellent accuracy and strong generalization,with an coefficient of determination of 0.986 5,mean absolute error of 2.154 9×104,and mean bias error of 3.418 2×106 on the test set.Furthermore,the proposed multi-objective genetic algorithm exhibited excellent global search capabilities,effectively avoiding local optima and identifying a smaller Pareto front.关键词
数据驱动/磁芯损耗建模/多目标遗传算法/磁芯损耗因素分析/深度神经网络/K-means聚类/轮廓系数法/功率变换器Key words
data-driven/magnetic core loss modeling/multi-objective genetic algorithm/core loss factor analysis/deep neural network/K-means clustering/silhouette coefficient method/power converters分类
信息技术与安全科学引用本文复制引用
黄问铉,曾浩政,林奕津,殷林飞..基于数据驱动方法的磁性元件损耗研究[J].综合智慧能源,2025,47(4):73-84,12.基金项目
国家自然科学基金项目(62463001) National Natural Science Foundation of China(62463001) （62463001）
