基于改进遗传算法的有源配电网电力恢复优化策略。

IF 3.9 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY
Pengpeng Lyu, Qiangsheng Bu, Yu Liu, Jiangping Jing, Jinfeng Hu, Lei Su, Yundi Chu
{"title":"基于改进遗传算法的有源配电网电力恢复优化策略。","authors":"Pengpeng Lyu, Qiangsheng Bu, Yu Liu, Jiangping Jing, Jinfeng Hu, Lei Su, Yundi Chu","doi":"10.3390/biomimetics10090618","DOIUrl":null,"url":null,"abstract":"<p><p>During feeder outages in the distribution network, localized power restoration using distribution resources (e.g., PVs) can ensure supply to critical loads and mitigate adverse impacts, especially when main grid support is unavailable. This study presents a power restoration strategy aiming at maximizing the restoration duration of critical loads to ensure their prioritized recovery, thereby significantly improving power system reliability. The methodology begins with load enumeration via breadth-first search (BFS) and utilizes a long short-term memory (LSTM) neural network to predict microgrid generation output. Then, an adaptive multipoint crossover genetic solving algorithm (AMCGA) is proposed, which can dynamically adjust crossover and mutation rates, enabling rapid convergence and requiring fewer parameters, thus optimizing island partitioning to prioritize critical load demands. Experimental results show that AMCGA improves convergence speed by 42.5% over the traditional genetic algorithm, resulting in longer restoration durations. Compared with other strategies that do not prioritize critical load recovery, the proposed strategy has shown superior performance in enhancing critical load restoration, optimizing island partitioning, and reducing recovery fluctuations, thereby confirming the strategy's effectiveness in maximizing restoration and improving stability.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":"10 9","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467194/pdf/","citationCount":"0","resultStr":"{\"title\":\"Power Restoration Optimization Strategy for Active Distribution Networks Using Improved Genetic Algorithm.\",\"authors\":\"Pengpeng Lyu, Qiangsheng Bu, Yu Liu, Jiangping Jing, Jinfeng Hu, Lei Su, Yundi Chu\",\"doi\":\"10.3390/biomimetics10090618\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>During feeder outages in the distribution network, localized power restoration using distribution resources (e.g., PVs) can ensure supply to critical loads and mitigate adverse impacts, especially when main grid support is unavailable. This study presents a power restoration strategy aiming at maximizing the restoration duration of critical loads to ensure their prioritized recovery, thereby significantly improving power system reliability. The methodology begins with load enumeration via breadth-first search (BFS) and utilizes a long short-term memory (LSTM) neural network to predict microgrid generation output. Then, an adaptive multipoint crossover genetic solving algorithm (AMCGA) is proposed, which can dynamically adjust crossover and mutation rates, enabling rapid convergence and requiring fewer parameters, thus optimizing island partitioning to prioritize critical load demands. Experimental results show that AMCGA improves convergence speed by 42.5% over the traditional genetic algorithm, resulting in longer restoration durations. Compared with other strategies that do not prioritize critical load recovery, the proposed strategy has shown superior performance in enhancing critical load restoration, optimizing island partitioning, and reducing recovery fluctuations, thereby confirming the strategy's effectiveness in maximizing restoration and improving stability.</p>\",\"PeriodicalId\":8907,\"journal\":{\"name\":\"Biomimetics\",\"volume\":\"10 9\",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467194/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomimetics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/biomimetics10090618\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomimetics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/biomimetics10090618","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

在配电网中的馈线中断期间,利用配电资源(例如pv)进行局部电力恢复可以确保对关键负载的供应并减轻不利影响,特别是在主电网支持不可用的情况下。本研究提出了一种以最大化关键负荷恢复时间为目标的电力恢复策略,以保证关键负荷优先恢复,从而显著提高电力系统的可靠性。该方法首先通过广度优先搜索(BFS)进行负荷枚举,然后利用长短期记忆(LSTM)神经网络预测微电网发电输出。然后,提出了一种自适应多点交叉遗传求解算法(AMCGA),该算法可以动态调整交叉和突变率,收敛速度快,所需参数少,从而优化孤岛划分,优先考虑关键负载需求。实验结果表明,AMCGA的收敛速度比传统遗传算法提高了42.5%,恢复时间更长。与其他未优先考虑关键负载恢复的策略相比,本文提出的策略在增强关键负载恢复、优化孤岛划分、减少恢复波动方面表现出了更优的性能,从而证实了该策略在最大限度地恢复和提高稳定性方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Power Restoration Optimization Strategy for Active Distribution Networks Using Improved Genetic Algorithm.

Power Restoration Optimization Strategy for Active Distribution Networks Using Improved Genetic Algorithm.

Power Restoration Optimization Strategy for Active Distribution Networks Using Improved Genetic Algorithm.

Power Restoration Optimization Strategy for Active Distribution Networks Using Improved Genetic Algorithm.

During feeder outages in the distribution network, localized power restoration using distribution resources (e.g., PVs) can ensure supply to critical loads and mitigate adverse impacts, especially when main grid support is unavailable. This study presents a power restoration strategy aiming at maximizing the restoration duration of critical loads to ensure their prioritized recovery, thereby significantly improving power system reliability. The methodology begins with load enumeration via breadth-first search (BFS) and utilizes a long short-term memory (LSTM) neural network to predict microgrid generation output. Then, an adaptive multipoint crossover genetic solving algorithm (AMCGA) is proposed, which can dynamically adjust crossover and mutation rates, enabling rapid convergence and requiring fewer parameters, thus optimizing island partitioning to prioritize critical load demands. Experimental results show that AMCGA improves convergence speed by 42.5% over the traditional genetic algorithm, resulting in longer restoration durations. Compared with other strategies that do not prioritize critical load recovery, the proposed strategy has shown superior performance in enhancing critical load restoration, optimizing island partitioning, and reducing recovery fluctuations, thereby confirming the strategy's effectiveness in maximizing restoration and improving stability.

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
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信