基于粒子群算法的汽轮发电机组负荷分配优化

2011 Fourth International Conference on Intelligent Computation Technology and Automation Pub Date : 2011-03-28 DOI:10.1109/ICICTA.2011.11

Yan Tao, Xu Jiatian, J. Weiguo

{"title":"基于粒子群算法的汽轮发电机组负荷分配优化","authors":"Yan Tao, Xu Jiatian, J. Weiguo","doi":"10.1109/ICICTA.2011.11","DOIUrl":null,"url":null,"abstract":"In concern with the slow-convergence disadvantage of GA, PSO is combined with GA in this paper for a load distribution optimization among turbine-generators. By comparison with GA method, it is shown that PSO-GA is better than the GA method in the aspect of calculation speed, bearing excellent stability and fast convergence.","PeriodicalId":368130,"journal":{"name":"2011 Fourth International Conference on Intelligent Computation Technology and Automation","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A Load Distribution Optimization among Turbine-generators Based on PSO-GA\",\"authors\":\"Yan Tao, Xu Jiatian, J. Weiguo\",\"doi\":\"10.1109/ICICTA.2011.11\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In concern with the slow-convergence disadvantage of GA, PSO is combined with GA in this paper for a load distribution optimization among turbine-generators. By comparison with GA method, it is shown that PSO-GA is better than the GA method in the aspect of calculation speed, bearing excellent stability and fast convergence.\",\"PeriodicalId\":368130,\"journal\":{\"name\":\"2011 Fourth International Conference on Intelligent Computation Technology and Automation\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-03-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Fourth International Conference on Intelligent Computation Technology and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICTA.2011.11\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Fourth International Conference on Intelligent Computation Technology and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICTA.2011.11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

摘要

针对遗传算法收敛速度慢的缺点，本文将粒子群算法与遗传算法相结合，用于汽轮发电机组间负荷分配优化。通过与遗传算法的比较，表明PSO-GA算法在计算速度快、稳定性好、收敛速度快等方面优于遗传算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A Load Distribution Optimization among Turbine-generators Based on PSO-GA

In concern with the slow-convergence disadvantage of GA, PSO is combined with GA in this paper for a load distribution optimization among turbine-generators. By comparison with GA method, it is shown that PSO-GA is better than the GA method in the aspect of calculation speed, bearing excellent stability and fast convergence.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2011 Fourth International Conference on Intelligent Computation Technology and Automation

自引率

0.00%

发文量