A Diversified Multiobjective Simulated Annealing and Genetic Algorithm for Optimizing a Three-Phase HTS Transformer

被引：19

作者：

Daneshmand, Shabnam V. ^{[1
]}

Heydari, Hossein ^{[2
]}

机构：

[1] MAPNA Grp, R&D Dept, Tehran 1918953651, Iran

[2] IUST, Ctr Excellence Power Syst Automat & Operat, Elect Engn Dept, Tehran 1684613114, Iran

来源：

IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY | 2016年 / 26卷 / 02期

关键词：

Genetic algorithm (GA); high-temperature superconducting (HTS) transformer; multiobjective optimization; simulated annealing (SA); OPTIMAL-DESIGN METHOD; SUPERCONDUCTING MAGNETS; HYSTERESIS LOSSES; OPTIMIZATION; AC; FIELD; COILS;

D O I：

10.1109/TASC.2016.2519420

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, a diversified multiobjective optimization of a transformer built from high-temperature superconducting (HTS) windings is presented. The main goal is an effective approach for an optimal HTS transformer design that involves the determination of selective transformer parameters when selected objectives are optimized. However, multiobjective optimization parameters are usually complex functions of the design variables and available only froman analysis of a finite-elementmodel of the structure. As such, this requires the need for advanced numerical techniques for simulation and analysis of the HTS transformer by FLUX software. In addition, Python software is used along with two-dimensional FLUX for running the optimal design concepts based on simulated annealing and the genetic algorithm for the multiobjective optimization of the HTS transformer, which is the main motivation of this paper.

引用

页数：10

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