Topology Identification of Low-Voltage Power Lines Based on IEC 61850 and the Clustering Method

Author:

Sun LingyanORCID,Chen Yu,Du Qinjun,Ding Rui,Liu Zhidong,Cheng Qian

Abstract

The large-scale access of distributed power puts forward higher requirements for the monitoring of the distribution networks, and the topology identification of low-voltage power lines can effectively promote the integration of monitoring data and the distribution network information, effectively realizing the rapid identification of faults and ensuring the safety of users. In this paper, the method of graph theory was used to simplify the analysis of low-voltage lines, and the full topology identification strategy was proposed. Based on IEC 61850 SCL topology configuration information, line topology identification within the region was realized, and the correlation between regions was determined by the injection method. According to the configuration information, regional association information, and user’s collection information, the low-voltage station area line topology was divided into known regional topology and unknown regional topology. Aiming for the identification of line topology in the unknown region, according to the similarity of voltage fluctuations over short electrical distances, clustering analysis of user’s voltage data in the unknown region was carried out based on the k-means clustering algorithm. The test results showed that this scheme can realize the identification of topology in the region.

Funder

National Key R&D Program of China

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3