Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks

For low-voltage distribution networks (LVDNs), accurate models depicting network and phase connectivity are crucial to the analysis, planning, and operation of these networks. However, phase connectivity data in the LVDN are usually incorrect or missing. Wrong or incomplete phase information collect...

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Autores principales:	Huang Yu, Yufeng Wu, Weiling Guan, Daolu Zhang, Tao Yu, Qianjin Liu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	phase identification DBSCAN cluster smart meter low-voltage distribution network non-linear dimensionality reduction algorithm General Works A
Acceso en línea:	https://doaj.org/article/c6e256b50ab04051a629a2aaab7a9e71
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spelling	oai:doaj.org-article:c6e256b50ab04051a629a2aaab7a9e712021-11-18T06:42:32ZPractical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks2296-598X10.3389/fenrg.2021.752571https://doaj.org/article/c6e256b50ab04051a629a2aaab7a9e712021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fenrg.2021.752571/fullhttps://doaj.org/toc/2296-598XFor low-voltage distribution networks (LVDNs), accurate models depicting network and phase connectivity are crucial to the analysis, planning, and operation of these networks. However, phase connectivity data in the LVDN are usually incorrect or missing. Wrong or incomplete phase information collected could lead to unbalanced operation of three-phase distribution systems and increased power loss. Based on the advanced measurement infrastructure (AMI) in the development of smart grids, in this study, a novel data-driven phase identification algorithm is proposed. Firstly, the method involves extracting features from voltage–time matrices using a non-linear dimension reduction algorithm. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to divide customers into clusters with arbitrary shape. Finally, the algorithms were tested with the IEEE European Low Voltage Test Feeder of the IEEE PES AMPS DSAS Test Feeder working group. The results showed an accuracy of over 90% for the method.Huang YuHuang YuYufeng WuYufeng WuWeiling GuanWeiling GuanDaolu ZhangDaolu ZhangTao YuTao YuQianjin LiuQianjin LiuFrontiers Media S.A.articlephase identificationDBSCAN clustersmart meterlow-voltage distribution networknon-linear dimensionality reduction algorithmGeneral WorksAENFrontiers in Energy Research, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	phase identification DBSCAN cluster smart meter low-voltage distribution network non-linear dimensionality reduction algorithm General Works A
spellingShingle	phase identification DBSCAN cluster smart meter low-voltage distribution network non-linear dimensionality reduction algorithm General Works A Huang Yu Huang Yu Yufeng Wu Yufeng Wu Weiling Guan Weiling Guan Daolu Zhang Daolu Zhang Tao Yu Tao Yu Qianjin Liu Qianjin Liu Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks
description	For low-voltage distribution networks (LVDNs), accurate models depicting network and phase connectivity are crucial to the analysis, planning, and operation of these networks. However, phase connectivity data in the LVDN are usually incorrect or missing. Wrong or incomplete phase information collected could lead to unbalanced operation of three-phase distribution systems and increased power loss. Based on the advanced measurement infrastructure (AMI) in the development of smart grids, in this study, a novel data-driven phase identification algorithm is proposed. Firstly, the method involves extracting features from voltage–time matrices using a non-linear dimension reduction algorithm. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to divide customers into clusters with arbitrary shape. Finally, the algorithms were tested with the IEEE European Low Voltage Test Feeder of the IEEE PES AMPS DSAS Test Feeder working group. The results showed an accuracy of over 90% for the method.
format	article
author	Huang Yu Huang Yu Yufeng Wu Yufeng Wu Weiling Guan Weiling Guan Daolu Zhang Daolu Zhang Tao Yu Tao Yu Qianjin Liu Qianjin Liu
author_facet	Huang Yu Huang Yu Yufeng Wu Yufeng Wu Weiling Guan Weiling Guan Daolu Zhang Daolu Zhang Tao Yu Tao Yu Qianjin Liu Qianjin Liu
author_sort	Huang Yu
title	Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks
title_short	Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks
title_full	Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks
title_fullStr	Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks
title_full_unstemmed	Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks
title_sort	practical method for data-driven user phase identification in low-voltage distribution networks
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/c6e256b50ab04051a629a2aaab7a9e71
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Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks

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