配电网优化重构的改进遗传算法

B. Radha, R. King, H. Rughooputh
{"title":"配电网优化重构的改进遗传算法","authors":"B. Radha, R. King, H. Rughooputh","doi":"10.1109/CEC.2003.1299844","DOIUrl":null,"url":null,"abstract":"Network reconfiguration in distribution systems is realised by changing the status of sectionalizing switches and is usually done for loss reduction. The distribution reconfiguration belongs to a complex combinatorial optimization problem. This is because there are multiple constraints, which must not be violated while finding an optimal or near-optimal solution to the distribution network reconfiguration problem. An exhaustive search can definitely find the optimal solution but is computationally intensive. Moreover, solution produced by other heuristic search techniques often produce local optima. Consequently, to solve the problem with implementation simplicity, computation efficiency, solution feasibility and optimality, an improved method based on a modified genetic algorithm (GA) with real valued genes and an adaptive mutation rate is used. The distribution network reconfiguration (DNRC) model, in which the objective is to minimize the system power loss, is presented in this paper with application to 16-bus, 33-bus systems and a real distribution network of Mauritius.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"31","resultStr":"{\"title\":\"A modified genetic algorithm for optimal electrical distribution network reconfiguration\",\"authors\":\"B. Radha, R. King, H. Rughooputh\",\"doi\":\"10.1109/CEC.2003.1299844\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Network reconfiguration in distribution systems is realised by changing the status of sectionalizing switches and is usually done for loss reduction. The distribution reconfiguration belongs to a complex combinatorial optimization problem. This is because there are multiple constraints, which must not be violated while finding an optimal or near-optimal solution to the distribution network reconfiguration problem. An exhaustive search can definitely find the optimal solution but is computationally intensive. Moreover, solution produced by other heuristic search techniques often produce local optima. Consequently, to solve the problem with implementation simplicity, computation efficiency, solution feasibility and optimality, an improved method based on a modified genetic algorithm (GA) with real valued genes and an adaptive mutation rate is used. The distribution network reconfiguration (DNRC) model, in which the objective is to minimize the system power loss, is presented in this paper with application to 16-bus, 33-bus systems and a real distribution network of Mauritius.\",\"PeriodicalId\":416243,\"journal\":{\"name\":\"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.\",\"volume\":\"84 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"31\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2003.1299844\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2003.1299844","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 31

摘要

配电系统中的网络重构是通过改变分网开关的状态来实现的,通常是为了减少损耗。分布重构属于一个复杂的组合优化问题。这是因为在寻找配电网重构问题的最优或接近最优解决方案时,存在多个约束,不能违反这些约束。穷举搜索肯定能找到最优解,但计算量很大。此外,其他启发式搜索技术产生的解往往产生局部最优。因此,为了使问题实现简单、计算效率高、解可行且最优,采用了一种基于实数基因和自适应突变率的改进遗传算法(GA)。本文以毛里求斯16总线、33总线系统和实际配电网为例,提出了以系统功率损耗最小为目标的配电网重构模型。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A modified genetic algorithm for optimal electrical distribution network reconfiguration
Network reconfiguration in distribution systems is realised by changing the status of sectionalizing switches and is usually done for loss reduction. The distribution reconfiguration belongs to a complex combinatorial optimization problem. This is because there are multiple constraints, which must not be violated while finding an optimal or near-optimal solution to the distribution network reconfiguration problem. An exhaustive search can definitely find the optimal solution but is computationally intensive. Moreover, solution produced by other heuristic search techniques often produce local optima. Consequently, to solve the problem with implementation simplicity, computation efficiency, solution feasibility and optimality, an improved method based on a modified genetic algorithm (GA) with real valued genes and an adaptive mutation rate is used. The distribution network reconfiguration (DNRC) model, in which the objective is to minimize the system power loss, is presented in this paper with application to 16-bus, 33-bus systems and a real distribution network of Mauritius.
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
自引率
0.00%
发文量
0
期刊最新文献
Searching oligo sets of human chromosome 12 using evolutionary strategies A nonlinear control system design based on HJB/HJI/FBI equations via differential genetic programming approach Particle swarm optimizers for Pareto optimization with enhanced archiving techniques Epigenetic programming: an approach of embedding epigenetic learning via modification of histones in genetic programming A new particle swarm optimiser for linearly constrained optimisation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1