利用进化算法优化风车农场模式

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation Pub Date : 2014-07-12 DOI:10.1145/2598394.2598506

C. Vanaret, N. Durand, J. Alliot

{"title":"利用进化算法优化风车农场模式","authors":"C. Vanaret, N. Durand, J. Alliot","doi":"10.1145/2598394.2598506","DOIUrl":null,"url":null,"abstract":"When designing a wind farm layout, we can reduce the number of variables by optimizing a pattern instead of considering the position of each turbine. In this paper we show that, by reducing the problem to only two variables defining a grid, we can gain up to $3\\%$ of energy output on simple examples of wind farms dealing with many turbines (up to 1000) while dramatically reducing the computation time.","PeriodicalId":298232,"journal":{"name":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Windmill farm pattern optimization using evolutionary algorithms\",\"authors\":\"C. Vanaret, N. Durand, J. Alliot\",\"doi\":\"10.1145/2598394.2598506\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When designing a wind farm layout, we can reduce the number of variables by optimizing a pattern instead of considering the position of each turbine. In this paper we show that, by reducing the problem to only two variables defining a grid, we can gain up to $3\\\\%$ of energy output on simple examples of wind farms dealing with many turbines (up to 1000) while dramatically reducing the computation time.\",\"PeriodicalId\":298232,\"journal\":{\"name\":\"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2598394.2598506\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2598394.2598506","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

摘要

在设计风电场布局时，我们可以通过优化一个模式来减少变量的数量，而不是考虑每个涡轮机的位置。在本文中，我们表明，通过将问题简化为定义电网的两个变量，我们可以在处理许多涡轮机(多达1000台)的风电场的简单示例中获得高达3%的能量输出，同时大大减少了计算时间。

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

微信好友朋友圈 QQ好友复制链接

本刊更多论文

Windmill farm pattern optimization using evolutionary algorithms

When designing a wind farm layout, we can reduce the number of variables by optimizing a pattern instead of considering the position of each turbine. In this paper we show that, by reducing the problem to only two variables defining a grid, we can gain up to $3\%$ of energy output on simple examples of wind farms dealing with many turbines (up to 1000) while dramatically reducing the computation time.

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

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

自引率

0.00%

发文量

期刊最新文献

Evolutionary synthesis of dynamical systems: the past, current, and future Incremental evolution of HERCL programs for robust control Selecting evolutionary operators using reinforcement learning: initial explorations Flood evolution: changing the evolutionary substrate from a path of stepping stones to a field of rocks Artificial immune systems for optimisation