Abstract

Accurately identifying the connectivity between transformers and downstream three-phase customers in low-voltage distribution networks is challenging, because voltage curves of different phases and nearby nodes can be weakly distinguishable, especially when adjacent transformers on the same feeder serve geographically close customers with highly similar voltage curves. This paper proposes a novel method based on load-switching fluctuation characteristics recorded by smart meters. By extracting localized current and voltage fluctuations and establishing correlation matching, the method overcomes the limited discriminability using steady-state measurements. The method operates in two stages: first, switching-induced fluctuation characteristics are extracted and matched to cluster customers by the supplying transformer and phase; second, cross-phase fluctuation characteristics are exploited to merge the above clusters into complete transformer-customer groups. Case studies demonstrate that the proposed method improves the accuracy of connectivity identification compared to traditional correlation-based methods, particularly in scenarios with highly similar voltage curves, and remains reliable under typical practical conditions.

Department(s)

Electrical and Computer Engineering

Publication Status

Early Access

Keywords and Phrases

adjacent transformer supply area; correlation matching; fluctuation characteristic; hierarchical clustering; Low-voltage distribution network; transformer-customer relationship identification

International Standard Serial Number (ISSN)

1949-3061; 1949-3053

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2026

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