A topology identification method based on one-dimensional convolutional neural network for distribution network

被引：5

作者：

Ni, Jielong ^{[1
]}

Tang, Zao ^{[1
]}

Liu, Jia ^{[1
]}

Zeng, Pingliang ^{[1
]}

Baldorj, Chimeddorj ^{[2
]}

机构：

[1] Hangzhou Dianzi Univ, Dept Automat, Hangzhou 310038, Peoples R China

[2] NovaTerra LLC, Ayud Tower Suite 1506 Sukhbaatar Dist,1 Khoroo, Ulaanbaatar 14240, Mongolia

来源：

ENERGY REPORTS | 2023年 / 9卷

基金：

国家重点研发计划;

关键词：

One-dimensional convolutional neural network; Active distribution network; Topology identification; DPMU; Node characteristics;

D O I：

10.1016/j.egyr.2022.11.008

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 network (DN) topology identification (TI) is the basis of distribution network state estimation. However, the connection of high-penetration renewable energy makes TI of DN more challenging. Thus, a TI method of active distribution network (ADN) based on one-dimensional convolutional neural network is proposed in this manuscript. Based on the sensitivity of node voltage to topology changing of DN, the characteristics of nodes are analyzed to select the key nodes to place the distribution network phasor measurement unit (DPMU), which can save investment and reduce the redundancy of model training. Several tests are carried out with the modified IEEE-33 bus DN with photovoltaic (PV) units. The results show that the proposed distribution network topology identification method can realize the high accuracy TI in ADN under limited DPMU measurement. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

引用

页码：355 / 362

页数：8

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