LAPSE:2024.0952
Published Article

LAPSE:2024.0952
Line−Household Relationship Identification Method for a Low-Voltage Distribution Network Based on Voltage Clustering and Electricity Consumption Characteristics
June 7, 2024
Abstract
To address the issue of inconspicuous electricity consumption characteristics among vacant users in low-voltage distribution networks (LVDNs), which hinders effective line−household relationship identification (LHRI), a method for identifying line−household relationship based on voltage clustering and electricity consumption characteristics is proposed. Initially, the paper employs Dynamic Time Warping (DTW) to analyze the similarity of user voltage profiles and utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster users. This approach identifies the topological relationship between vacant users and regular users to obtain multiple user categories. Subsequently, by analyzing the electricity consumption characteristic, the connection relationships between different user categories and phase lines are clarified based on the correlation between the electricity consumption characteristic vector of phase lines and the electricity consumption characteristic vector of user categories, thereby revealing the line−household relationship for all users. On the test dataset, the LHRI algorithm proposed in this article achieved 100% accuracy, within an allowable error range of 0.2%, and improved the accuracy by 20% compared to the traditional identification method. Finally, the LVDN simulation model established by OpenDSS 9.4.0.3 was used to verify the effectiveness of the proposed method, confirming its potential and advantages in practical applications.
To address the issue of inconspicuous electricity consumption characteristics among vacant users in low-voltage distribution networks (LVDNs), which hinders effective line−household relationship identification (LHRI), a method for identifying line−household relationship based on voltage clustering and electricity consumption characteristics is proposed. Initially, the paper employs Dynamic Time Warping (DTW) to analyze the similarity of user voltage profiles and utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster users. This approach identifies the topological relationship between vacant users and regular users to obtain multiple user categories. Subsequently, by analyzing the electricity consumption characteristic, the connection relationships between different user categories and phase lines are clarified based on the correlation between the electricity consumption characteristic vector of phase lines and the electricity consumption characteristic vector of user categories, thereby revealing the line−household relationship for all users. On the test dataset, the LHRI algorithm proposed in this article achieved 100% accuracy, within an allowable error range of 0.2%, and improved the accuracy by 20% compared to the traditional identification method. Finally, the LVDN simulation model established by OpenDSS 9.4.0.3 was used to verify the effectiveness of the proposed method, confirming its potential and advantages in practical applications.
Record ID
Keywords
electricity consumption characteristic, line–household relationship, low-voltage distribution network, vacant users, voltage clustering
Subject
Suggested Citation
Yao L, Huang J, Zhang W. Line−Household Relationship Identification Method for a Low-Voltage Distribution Network Based on Voltage Clustering and Electricity Consumption Characteristics. (2024). LAPSE:2024.0952
Author Affiliations
Yao L: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Huang J: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Zhang W: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China [ORCID]
Huang J: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Zhang W: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China [ORCID]
Journal Name
Processes
Volume
12
Issue
2
First Page
288
Year
2024
Publication Date
2024-01-28
ISSN
2227-9717
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Original Submission
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PII: pr12020288, Publication Type: Journal Article
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LAPSE:2024.0952
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https://doi.org/10.3390/pr12020288
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[v1] (Original Submission)
Jun 7, 2024
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Jun 7, 2024
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