Application research based on improved genetic algorithm for optimum design of power transformers

ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501) Pub Date : 2001-08-18 DOI:10.1109/ICEMS.2001.970657

Li Hui, Han Li, He Bei, Yang Shunchang

{"title":"Application research based on improved genetic algorithm for optimum design of power transformers","authors":"Li Hui, Han Li, He Bei, Yang Shunchang","doi":"10.1109/ICEMS.2001.970657","DOIUrl":null,"url":null,"abstract":"In order to attain global optimal or quasioptimum solution for power transformers design, some interrelated key techniques such as encoding scheme, genetic operators, constrained condition, fitness function for the simple genetic algorithm (SGA) are further reformed and researched. An improved genetic algorithm (IGA) is developed in this paper and applied to the optimum design of S9 power transformers for the first time. In addition, a multi-objective algorithm based on IGA is applied successfully in the double objective optimum design of S9 power transformers, by using the theory of variable weight coefficients for the multi-objective optimization. All the achievements in the paper are verified by a representative mathematical example and a practical S9-1000/10 kV power transformer. All the optimization results are satisfactory and show that IGA has powerful ability of global searching, excellent solution precision and has a bright application prospect in the fields of power transformers design.","PeriodicalId":143007,"journal":{"name":"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"53","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEMS.2001.970657","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 53

Abstract

In order to attain global optimal or quasioptimum solution for power transformers design, some interrelated key techniques such as encoding scheme, genetic operators, constrained condition, fitness function for the simple genetic algorithm (SGA) are further reformed and researched. An improved genetic algorithm (IGA) is developed in this paper and applied to the optimum design of S9 power transformers for the first time. In addition, a multi-objective algorithm based on IGA is applied successfully in the double objective optimum design of S9 power transformers, by using the theory of variable weight coefficients for the multi-objective optimization. All the achievements in the paper are verified by a representative mathematical example and a practical S9-1000/10 kV power transformer. All the optimization results are satisfactory and show that IGA has powerful ability of global searching, excellent solution precision and has a bright application prospect in the fields of power transformers design.

查看原文本刊更多论文

基于改进遗传算法的电力变压器优化设计应用研究

为了获得电力变压器设计的全局最优或准最优解，对简单遗传算法(SGA)的编码方案、遗传算子、约束条件、适应度函数等相关关键技术进行了进一步改革和研究。本文提出了一种改进的遗传算法，并首次应用于S9型电力变压器的优化设计。此外，利用变权系数多目标优化理论，将基于IGA的多目标优化算法成功应用于S9型电力变压器的双目标优化设计中。通过一个代表性的数学算例和s9 -1000/ 10kv电力变压器的实际应用验证了本文的所有成果。结果表明，该算法具有强大的全局搜索能力，求解精度高，在电力变压器设计领域具有广阔的应用前景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)

自引率

0.00%

发文量