高电压技术2025，Vol.51Issue(10)：5166-5174,9.DOI:10.13336/j.1003-6520.hve.20241143
基于LS-SVM与NSGA-Ⅱ的高频变压器优化设计方法
Optimal Design Method of High-frequency Transformer Based on LS-SVM and NSGA-Ⅱ
摘要
Abstract
It is of great significance to accurately optimize the design of high-frequency transformer(HFT)to improve the comprehensive performance of itself and its system.However,the existing HFT optimization design methods usually use simplified analytical formulas to calculate electromagnetic and thermal parameters such as core,winding loss and hot-spot temperature,although the calculation speed of these analytical formulas is fast,their accuracy is low,which leads to the low accuracy and reliability of the optimized design results.In order to take both the accuracy and speed of HFT optimization design into account,this paper introduces a surrogate model of HFT electromagnetic thermal parameters based on least squares support vector machines(LS-SVM)for the first time,and proposes a new HFT optimization design method based on non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ).In order to verify the superiority of the LS-SVM surrogate model proposed in this paper,the results show that the LS-SVM has higher accuracy compared with the recur-rent neural network and deep neural network surrogate model.Finally,based on the proposed optimization design method,a 5 kHz/10 kW HFT is optimized using multiple objectives,and the optimal design scheme is verified by finite elements method,and the results show that the proposed surrogate model of core loss,winding loss and hot-spot temperature has lower errors compared to the corresponding traditional analytical ones,with errors of 2.77％,3.03％and 0.92％respec-tively.Thus the accuracy and reliability of the proposed optimization design method is verified.关键词
高频变压器/高频损耗/热点温度/最小二乘支持向量机/多目标优化设计Key words
high-frequency transformers/high-frequency losses/hot spot temperature/least-squares support vector ma-chines/multi-objective optimization design引用本文复制引用
刘任,李常建,唐波..基于LS-SVM与NSGA-Ⅱ的高频变压器优化设计方法[J].高电压技术,2025,51(10):5166-5174,9.基金项目
国家自然科学基金青年科学基金(52407009).Project supported by National Natural Science Foundation of China for Young Talents(52407009). （52407009）
