Topology identification method for residential areas in low-voltage distribution networks based on unsupervised learning and graph theory

被引：19

作者：

Li, Haifeng ^{[1
]}

Liang, Wenzhao ^{[1
]}

Liang, Yuansheng ^{[1
]}

Li, Zhikeng ^{[2
]}

Wang, Gang ^{[1
]}

机构：

[1] South China Univ Technol, Sch Elect Power Engn, Guangzhou 510640, Peoples R China

[2] Guangdong Elect Power Design Inst Co Ltd, China Energy Engn Grp, Guangzhou 510663, Peoples R China

来源：

ELECTRIC POWER SYSTEMS RESEARCH | 2023年 / 215卷

关键词：

Low-voltage distribution network; Topology identification; Unsupervised learning; The tSNE-DBSCAN-LLE algorithm; Graph theory; ANALYTICS;

D O I：

10.1016/j.epsr.2022.108969

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The information of a low-voltage (LV) distribution network is important for power supply departments to monitor grid information, analyze faults and optimize grid operation status. However, the current mainstream methods are not able to comprehensively update the topology information of LV distribution networks in real time. Therefore, this paper proposes an unsupervised learning and graph theory-based method to identify four -level topology information and generate a topology diagram for low-voltage distribution network. Firstly, four -level topology information are identified based on the tSNE-DBSCAN-LLE algorithm. Then, the identied infor-mation is used to simply generate a topology diagram. Finally, the simulation data and the actual data from three LV distribution networks are analyzed to show the effectiveness and advantageousness of the proposed method.

引用

页数：15

共 34 条

[1] A new data-driven method based on Niching Genetic Algorithms for phase and substation identification [J].

Adrian Jimenez, Victor ;

Will, Adrian .

ELECTRIC POWER SYSTEMS RESEARCH, 2021, 199

[2] On Identification of Distribution Grids [J].

Ardakanian, Omid ;

Wong, Vincent W. S. ;

Dobbe, Roel ;

Low, Steven H. ;

von Meier, Alexandra ;

Tomlin, Claire J. ;

Yuan, Ye .

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS, 2019, 6 (03) :950-960

[3]

Arya V., 2011, 2011 IEEE Second International Conference on Smart Grid Communications (SmartGridComm 2011), P25, DOI 10.1109/SmartGridComm.2011.6102329

[4] Lessons learnt from real-time monitoring of the low voltage distribution network [J].

Barbato, Antimo ;

Dede, Alessio ;

Della Giustina, Davide ;

Massa, Giovanni ;

Angioni, Andrea ;

Lipari, Gianluca ;

Ponci, Ferdinanda ;

Repo, Sami .

SUSTAINABLE ENERGY GRIDS & NETWORKS, 2018, 15 :76-85

[5] Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions [J].

Bhattarai, Bishnu P. ;

Paudyal, Sumit ;

Luo, Yusheng ;

Mohanpurkar, Manish ;

Cheung, Kwok ;

Tonkoski, Reinaldo ;

Hovsapian, Rob ;

Myers, Kurt S. ;

Zhang, Rui ;

Zhao, Power ;

Manic, Milos ;

Zhang, Song ;

Zhang, Xiaping .

IET SMART GRID, 2019, 2 (02) :141-154

[6] Locally linear embedding: a survey [J].

Chen, Jing ;

Liu, Yang .

ARTIFICIAL INTELLIGENCE REVIEW, 2011, 36 (01) :29-48

[7] Low-voltage distribution network topology identification based on constrained least square and graph theory [J].

Cui, Shijie ;

Zeng, Peng ;

Song, Chunhe ;

Wang, Zhongfeng ;

Li, Guangye .

SOFT COMPUTING, 2022, 26 (17) :8509-8519

[8] Ethernet Topology Discovery for Networks With Incomplete Information [J].

Gobjuka, Hassan ;

Breitbart, Yuri J. .

IEEE-ACM TRANSACTIONS ON NETWORKING, 2010, 18 (04) :1220-1233

[9] Smart Meter Data-Based Three-Stage Algorithm to Calculate Power and Energy Losses in Low Voltage Distribution Networks [J].

Grigoras, Gheorghe ;

Neagu, Bogdan-Constantin .

ENERGIES, 2019, 12 (15)

[10]

Hee Jung Byun, 2018, Applied Mechanics and Materials, V878, P291, DOI [10.4028/www.scientific.net/amm.878.291, 10.4028/www.scientific.net/AMM.878.291]

← 1 2 3 4 →