Genetic Algorithm Based Multi Objective Optimization for Inductor Design

被引：0
|
作者：
Schobre, Thorben [1 ]
Ariztegui, Raquel Gonzalez [1 ]
Mallwitz, Regine [1 ]
机构：
[1] TU Braunschweig, Inst Elect Machines Tract & Drives, Hans Sommer Str 66, Braunschweig, Germany
来源：
2020 22ND EUROPEAN CONFERENCE ON POWER ELECTRONICS AND APPLICATIONS (EPE'20 ECCE EUROPE) | 2020年
关键词：
Converter circuit; Modeling; Design; Software; Artificial Intelligence;
D O I：
10.23919/epe20ecceeurope43536.2020.9215902
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：
In this work an optimal design approach for inductors for buck or boost converters is presented. The optimization routine is based on Non-Dominated Sorting Genetic Algorithm (NSGA2) which is well suited for such multi objective optimization applications with discrete design parameters. The optimization objectives are size and loss. Therefore an adequate loss model is established. The whole algorithm and the results are presented. To verify the models a test inductor is realized and characterized.
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页数：9
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