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Phase and Meter Box Identification for Single-phase Users Based on t-SNE Dimension Reduction and BIRCH Clustering
Author:
Affiliation:

1.College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;2.Electric Power Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310014, China;3.Haiyan Electric Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Jiaxing 314300, China;4.Zhejiang Huayun Information Technology Co., Ltd., Hangzhou 310012, China

Abstract:

The accurate phase and meter box information of single-phase users in low-voltage courts have great impacts on the checking of user-transformer relationships and the treatment and analysis of line losses. At present, the correction of topological documents mainly relies on the on-site checking by electrical engineers, which spends a lot of manpower and material resources with low efficiency. Therefore, it is necessary to study a more efficient method of checking the topological documents of the low-voltage courts. In this background, a phase and meter box identification method based on voltage measurement data of smart meters is proposed, which provides reference for topology identification and correction of low-voltage courts. Firstly, the t-distributed stochastic neighbor embedding (t-SNE) technology is adopted to reduce the dimension of original load data, so as to solve the redundancy problems caused by excessively high dimension of original load characteristics of users. Then, the balanced iterative reducing and clustering using hierarchies (BIRCH) method is used to cluster the dimension-reduced load data, so as to identify the phase and meter box information of single-phase users. Finally, case studies for an actual low-voltage court in Haining of Zhejiang Province, China, are performed to verify the correctness of the proposed method, and the results show that the proposed model is feasible and effective.

Keywords:

Foundation:

This work is supported by National Key R&D Program of China (No. 2016YFB0901100), National Natural Science Foundation of China (No. 51777185) and State Grid Corporation of China (No. 5600-201919168A-0-0-00).

Get Citation
[1]LIAN Zikuan, YAO Li, LIU Shengyuan, et al. Phase and Meter Box Identification for Single-phase Users Based on t-SNE Dimension Reduction and BIRCH Clustering[J]. Automation of Electric Power Systems,2020,44(8):176-184. DOI:10.7500/AEPS20190621001
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History
  • Received:June 21,2019
  • Revised:September 28,2019
  • Adopted:
  • Online: April 09,2020
  • Published: