[Poster Presentation]Identification Method for Household-Transformer Relationship in Low-voltage Transformer Area Based on LCSS-DBSCAN - Presentation details

Identification Method for Household-Transformer Relationship in Low-voltage Transformer Area Based on LCSS-DBSCAN
ID:48 Submission ID:32 View Protection:ATTENDEE Updated Time:2022-10-14 10:05:14 Hits:318 Poster Presentation

Start Time:2022-11-04 10:42 (Asia/Shanghai)

Duration:12min

Session:[S] Power System and Automation [PS1] Poster Session 1

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Abstract
Due to the lag in technology and management, there is a widespread problem of inaccurate relationship between transformers and users in distribution transformers in the low-voltage station area, resulting in abnormal line loss statistics and outages in the station area. Issues such as the untimely notification of power recovery have seriously hampered the improvement of the O&M management level and customer satisfaction with electricity consumption. The traditional way of sorting out the relationship between households in transformer area districts by manpower is difficult, costly, and the accuracy is difficult to guarantee. Therefore, by researching the similarity of low-voltage users' voltage time series, a method for identifying the relationship between low-voltage stations and households based on LCSS-DBSCAN is proposed. First, the collected voltage data of the transformer and user are verified, and then the similarity between the voltage sequences is solved by the longest common subsequence method; Clustering was carried out to complete the identification of household-transformer relationship. Finally, the actual data of a pilot station in Henan Province was selected as the analysis sample to verify the effectiveness of the proposed method.
Keywords
household-transformer relationship; self-check; LCSS; DBSCAN; low-voltage distribution network
Speaker
Wenjin Zou
Nanjing Normal University

Submission Author
Wenjin Zou Nanjing Normal University
Shaofei Hao Nanjing Normal University
Haoran Ge Nanjing Normal University
Yu Xia Nanjing Normal University
Gang Ma Nanjing Normal University
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