Distribution network topology error correction using smart meter data analytics

Luan, Wenpeng; Peng, Joshua; Maras, Mirjana; Lo, Joyce

doi:10.1109/pesmg.2013.6672786

Cited by 18 publications

(1 citation statement)

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“…A model has been established to identify transformers and user phases based on voltage correlation using linear regression (Short, 2013). The correlation between a circuit and transformers in it can be determined by analyzing the correlation in voltage between buses and user meters from the perspective of power flow (Luan et al, 2013;Tang and Milanovic, 2018). Topology can also be identified by analyzing the correlation in load between lines at upper and lower levels (Pappu et al, 2018;Lisowski et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Practical Method for Data-Driven User Phase Identification in Low-Voltage Distribution Networks

Guan

et al. 2021

Front. Energy Res.

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For low-voltage distribution networks (LVDNs), accurate models depicting network and phase connectivity are crucial to the analysis, planning, and operation of these networks. However, phase connectivity data in the LVDN are usually incorrect or missing. Wrong or incomplete phase information collected could lead to unbalanced operation of three-phase distribution systems and increased power loss. Based on the advanced measurement infrastructure (AMI) in the development of smart grids, in this study, a novel data-driven phase identification algorithm is proposed. Firstly, the method involves extracting features from voltage–time matrices using a non-linear dimension reduction algorithm. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to divide customers into clusters with arbitrary shape. Finally, the algorithms were tested with the IEEE European Low Voltage Test Feeder of the IEEE PES AMPS DSAS Test Feeder working group. The results showed an accuracy of over 90% for the method.

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