Phase Identification of LV Distribution Network with Smart Meter Data

被引:0
|
作者
Tang, Xiaoqing [1 ]
Milanovic, Jovica V. [1 ]
机构
[1] Univ Manchester, Sch Elect & Elect Engn, Manchester, Lancs, England
基金
欧盟地平线“2020”;
关键词
phase identification; smart meter; LV distribution network topology; LASSO method; REGRESSION SHRINKAGE; SELECTION;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Distribution networks require more operational flexibility these days than ever before due to growing penetration of low-carbon technologies such as solar energy and CHPs. The LV network topology information is essential for efficient network operation and planning. The customers at LV network are usually single phase loads which connect to either phase a, b, or c. However, these phase connection information that distribution network operators have, are not always available or accurate due to lack of data communication or changing of customer connections. The state-of-the-art phase identification methods are relying on using the smart meter data and main challenges their facing are low penetration/coverage of smart meters and high substation load measurement noises. This paper proposes a novel Least Absolute Shrinkage and Selection Operator (LASSO) based data-driven approach to identify customer phase connection in LV distribution network. The proposed method is validated on a real distribution network with 228 customers and achieved 97% accuracy with only 60% smart meter coverage with a low cost (class 5, i.e., +/- 5% error) measuring device for substation load measurement.
引用
收藏
页数:5
相关论文
共 50 条
  • [1] Smart Meter Traffic in a Real LV Distribution Network
    Andreadou, Nikoleta
    Kotsakis, Evangelos
    Masera, Marcelo
    [J]. ENERGIES, 2018, 11 (05)
  • [2] Smart Meter Data Analytics for Distribution Network
    Tang, Guojing
    Han, Yinghua
    Wang, Jinkuan
    Zhao, Qiang
    [J]. 2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 8882 - 8887
  • [3] Phase Identification in Electric Power Distribution Systems by Clustering of Smart Meter Data
    Wang, Wenyu
    Yu, Nanpeng
    Foggo, Brandon
    Davis, Joshua
    Li, Juan
    [J]. 2016 15TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2016), 2016, : 259 - 265
  • [4] A MILP Model for Phase Identification in LV Distribution Feeders Using Smart Meters Data
    Akhijahani, Adel Heidari
    Hojjatinejad, Saeed
    Safdarian, Amir
    [J]. 2019 SMART GRID CONFERENCE (SGC), 2019, : 199 - 204
  • [5] Using grouped smart meter data in phase identification
    Brint, Andrew
    Poursharif, Goudarz
    Black, Mary
    Marshall, Mark
    [J]. COMPUTERS & OPERATIONS RESEARCH, 2018, 96 : 213 - +
  • [6] Research on Phase identification in Distribution Area Based on Smart meter
    Liu, Bin
    Tan, Zhukui
    Zhang, Qiuyan
    Xu, Yutao
    Dai, Jiyulei
    [J]. 2022 9TH INTERNATIONAL FORUM ON ELECTRICAL ENGINEERING AND AUTOMATION, IFEEA, 2022, : 1131 - 1135
  • [7] A Review of Smart Meter Data Analytics for Distribution Network Applications
    Athanasiadis, Christos L.
    Papadopoulos, Theofilos A.
    Kryonidis, Georgios C.
    [J]. 2023 IEEE BELGRADE POWERTECH, 2023,
  • [8] Smart Meter Data Analytics for Distribution Network Connectivity Verification
    Luan, Wenpeng
    Peng, Joshua
    Maras, Mirjana
    Lo, Joyce
    Harapnuk, Brian
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2015, 6 (04) : 1964 - 1971
  • [9] Consumer Phase Identification in a Three Phase Unbalanced LV Distribution Network
    Pezeshki, H.
    Wolfs, P. J.
    [J]. 2012 3RD IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES EUROPE (ISGT EUROPE), 2012,
  • [10] Topology Identification Method of Distribution Network Based on Smart Meter Measurements
    Zhang, Mingze
    Luan, Wenpeng
    Guo, Shen
    Wang, Peng
    [J]. 2018 CHINA INTERNATIONAL CONFERENCE ON ELECTRICITY DISTRIBUTION (CICED), 2018, : 372 - 376