Novel Multiobjective Optimization of MF Transformers for Soft-Switching Converters using a Genetic Algorithm

被引：0

作者：

Garcia-Bediaga, Asier ^{[1
]}

Villar, Irma ^{[1
]}

Mir, Luis ^{[1
]}

Etxeberria-Otadui, Ion ^{[1
]}

Rufer, Alfred ^{[2
]}

机构：

[1] IK4 IKERLAN Technol Res Ctr, Control Engn & Power Elect Area, Arrasate Mondragon 20500, Spain

[2] Ecole Polytech Fed Lausanne, Ind Elect Lab, STI IEL LEI, CH-1015 Lausanne, Switzerland

来源：

2014 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE) | 2014年

关键词：

medium frequency; soft-switching; high-power density; power transformers; genetic algorithms; DESIGN;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper presents a novel computer-aided multiobjective optimal design for medium-frequency (MF) transformers using NSGA genetic algorithm. The proposed methodology has the aim of reaching the best MF transformer for a given power converter topology, by optimizing transformer efficiency, weight, and also, transformer leakage and magnetizing inductances at the same time. Moreover, this work provides some practices for a correct formulation of complex power-electronic design problems and the selection of the optimization algorithm. The proposed methodology and the optimal solutions are validated with the design and the development of two different 10 kVA/500V transformers and some experimental measurements are presented.

引用

页码：5368 / 5375

页数：8

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