改进具有相对平衡电源的配电网络拓扑识别

IF 1.6 Q4 ENERGY & FUELS
Wenpeng Luan, Da Xu, Bo Liu, Wenqian Jiang, Li Feng, Wenbin Liu
{"title":"改进具有相对平衡电源的配电网络拓扑识别","authors":"Wenpeng Luan,&nbsp;Da Xu,&nbsp;Bo Liu,&nbsp;Wenqian Jiang,&nbsp;Li Feng,&nbsp;Wenbin Liu","doi":"10.1049/esi2.12142","DOIUrl":null,"url":null,"abstract":"<p>Having correct distribution network topology information is essential for system state estimation, line loss analysis, electricity theft detection and fault location. At present, with continuous deployment of smart sensors, a large amount of monitoring data is collected, which enables refined management for distribution network. A data-driven low voltage (LV) distribution network topology identification method is proposed, which realises transformer-customer pairing and customer phase identification for distribution network with relatively balanced power supplies. Firstly, an integrated similarity coefficient of voltage curve is proposed, which can reflect the neighbourhood relationship within stations while increase the distinction between stations; the K-Nearest Neighbour (KNN) algorithm is used to propagate the service transformer labels to complete transformer-customer association. Then, the influence of power fluctuation on voltage curve is analysed and a dynamic sliding window model is adopted to search for voltage segments with significantly difference among three phase feeders to formulate a voltage time series to identify customer phase. Finally, the results are corrected and verified based on the principle of network power balance. The proposed algorithm is tested in two different real substations in China and Europe and shows high accuracy and robustness especially in distribution network with relatively balanced power supplies.</p>","PeriodicalId":33288,"journal":{"name":"IET Energy Systems Integration","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.12142","citationCount":"0","resultStr":"{\"title\":\"Improved topology identification for distribution network with relatively balanced power supplies\",\"authors\":\"Wenpeng Luan,&nbsp;Da Xu,&nbsp;Bo Liu,&nbsp;Wenqian Jiang,&nbsp;Li Feng,&nbsp;Wenbin Liu\",\"doi\":\"10.1049/esi2.12142\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Having correct distribution network topology information is essential for system state estimation, line loss analysis, electricity theft detection and fault location. At present, with continuous deployment of smart sensors, a large amount of monitoring data is collected, which enables refined management for distribution network. A data-driven low voltage (LV) distribution network topology identification method is proposed, which realises transformer-customer pairing and customer phase identification for distribution network with relatively balanced power supplies. Firstly, an integrated similarity coefficient of voltage curve is proposed, which can reflect the neighbourhood relationship within stations while increase the distinction between stations; the K-Nearest Neighbour (KNN) algorithm is used to propagate the service transformer labels to complete transformer-customer association. Then, the influence of power fluctuation on voltage curve is analysed and a dynamic sliding window model is adopted to search for voltage segments with significantly difference among three phase feeders to formulate a voltage time series to identify customer phase. Finally, the results are corrected and verified based on the principle of network power balance. The proposed algorithm is tested in two different real substations in China and Europe and shows high accuracy and robustness especially in distribution network with relatively balanced power supplies.</p>\",\"PeriodicalId\":33288,\"journal\":{\"name\":\"IET Energy Systems Integration\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/esi2.12142\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Energy Systems Integration\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/esi2.12142\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Energy Systems Integration","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/esi2.12142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

摘要

掌握正确的配电网络拓扑信息对于系统状态估计、线损分析、窃电检测和故障定位至关重要。目前,随着智能传感器的不断部署,大量监测数据被收集起来,实现了配电网的精细化管理。本文提出了一种数据驱动的低压配电网拓扑识别方法,实现了供电相对平衡的配电网的变压器-客户配对和客户相位识别。首先,提出了电压曲线的综合相似系数,既能反映站内的邻近关系,又能增加站与站之间的区别;利用 K-Nearest Neighbour(KNN)算法传播服务变压器标签,完成变压器与客户的关联。然后,分析电力波动对电压曲线的影响,并采用动态滑动窗口模型搜索三相馈线间差异显著的电压段,形成电压时间序列以识别客户相位。最后,根据网络功率平衡原理对结果进行修正和验证。所提出的算法在中国和欧洲的两个不同的实际变电站中进行了测试,显示出较高的准确性和鲁棒性,尤其是在供电相对平衡的配电网络中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Improved topology identification for distribution network with relatively balanced power supplies

Improved topology identification for distribution network with relatively balanced power supplies

Having correct distribution network topology information is essential for system state estimation, line loss analysis, electricity theft detection and fault location. At present, with continuous deployment of smart sensors, a large amount of monitoring data is collected, which enables refined management for distribution network. A data-driven low voltage (LV) distribution network topology identification method is proposed, which realises transformer-customer pairing and customer phase identification for distribution network with relatively balanced power supplies. Firstly, an integrated similarity coefficient of voltage curve is proposed, which can reflect the neighbourhood relationship within stations while increase the distinction between stations; the K-Nearest Neighbour (KNN) algorithm is used to propagate the service transformer labels to complete transformer-customer association. Then, the influence of power fluctuation on voltage curve is analysed and a dynamic sliding window model is adopted to search for voltage segments with significantly difference among three phase feeders to formulate a voltage time series to identify customer phase. Finally, the results are corrected and verified based on the principle of network power balance. The proposed algorithm is tested in two different real substations in China and Europe and shows high accuracy and robustness especially in distribution network with relatively balanced power supplies.

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
IET Energy Systems Integration
IET Energy Systems Integration Engineering-Engineering (miscellaneous)
CiteScore
5.90
自引率
8.30%
发文量
29
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:481959085
Book学术官方微信