Identification Method for User-transformer Relationship Based on Multi-feature Symbolic Aggregate Approximation and Hierarchical Clustering

被引：0

作者：

Zhou G. ^{[1
]}

Mao H. ^{[1
]}

Feng Y. ^{[1
]}

Hua J. ^{[2
]}

Zeng Y. ^{[3
]}

机构：

[1] School of Electrical Engineering, Southeast University, Nanjing

[2] State Grid Wuxi Power Supply Company of Jiangsu Electric Power Co., Ltd., Wuxi

[3] Guangdong Power Grid Co., Ltd., Guangzhou

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2024年 / 48卷 / 03期

关键词：

hierarchical clustering; low-voltage distribution station area; similarity of voltage curves; topology identification; user-transformer relationship;

D O I：

10.7500/AEPS20230419005

中图分类号：

学科分类号：

摘要：

In view of the possible problem of wrong user-transformer relationships in the topology file of the low-voltage distribution station area, this paper proposes a user-transformer relationship identification method based on multi-feature symbolic aggregate approximation (MF-SAX) and hierarchical clustering. First, the symbolic aggregate approximation expression method is used to convert the user voltage time series into string series, and two additional parameters, i. e., the voltage fluctuation coefficient and the voltage change trend, are introduced to strengthen its feature expression. Then, the similarity matrix of the user voltage curves is generated based on the edit distance, and the hierarchical clustering algorithm is combined to realize the identification of the user-transformer relationships. Finally, the results of the practical case show that, compared with some existing methods, the proposed method achieves higher accuracy and few false alarms, which can directly respond to missing data situations, and has higher efficiency. © 2024 Automation of Electric Power Systems Press. All rights reserved.

引用

页码：133 / 141

页数：8

共 27 条

[21]

RAHMAN M S., New efficient algorithms for the LCS and constrained LCS problems［J］, Information Processing Letters, 106, 1, pp. 13-18, (2008)

[22]

QIU Jing, OU Jindong, XIE Dong, Et al., A similarity measurement method for magnetic anomaly signal under low signal-to-noise based on orthogonal basis function-edit distance ［J］, Journal of Electronics & Information Technology, 44, 2, pp. 745-753, (2022)

[23]

DAI Xiang, HUANG Xifeng, TANG Rui, Et al., Subtopic detection algorithm based on hierarchical clustering［J］, Journal of South China University of Technology （Natural Science Edition）, 47, 8, pp. 84-95, (2019)

[24]

ZENG Taiying, DU Fei, Image super-resolution reconstruction based on hierarchical clustering［J］, Acta Optica Sinica, 38, 4, pp. 130-137, (2018)

[25]

HUANG J, NIE F P，, HUANG H，, Et al., Robust manifold nonnegative matrix factorization［J］, ACM Transactions on Knowledge Discovery from Data, 8, 3, pp. 1-21, (2014)

[26]

LI Keming, JIANG Yaqun, HUANG Shifu, Et al., Topology identification method of a low-voltage distribution station area based on DTW distance and cluster analysis［J］, Power System Protection and Control, 49, 14, pp. 29-36, (2021)

[27]

YIN Peng, LIANG Haishen, GU Zhicheng, Et al., Identification method of topology structure in distribution area based on Euclidean morphological distance and AP cluster analysis［J/OL］

← 1 2 3 →