TY - JOUR
T1 - Density-based clustering algorithm for associating transformers with smart meters via GPS-AMI data
AU - Cook, Elizabeth
AU - Saleem, Muhammad Bilal
AU - Weng, Yang
AU - Abate, Stephen
AU - Kelly-Pitou, Katrina
AU - Grainger, Brandon
N1 - Funding Information:
This work was supported in part by the Department of Energy under grants DE-AR00001858-1631 and DE-EE0009355 , the National Science Foundation (NSF) under the grants ECCS-1810537 and ECCS-2048288 .
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/11
Y1 - 2022/11
N2 - The ongoing deployment of Distributed Energy Resources, while bringing benefits, introduces significant challenges to the electric utility industry, especially in the distribution grid. These challenges call for closer monitoring through state estimation, where real-time topology recovery is the basis for accurate modeling. Previous methods either ignore geographical information, which is important in connectivity identification or are based on an ideal assumption of an isolated sub-network for topology recovery, e.g., within one transformer. This requires field engineers to identify the association, which is costly and may contain errors. To solve these problems, we propose a density-based topology clustering method that leverages both voltage domain data and the geographical space information to segment datasets from a large utility customer pool, after which other topology reconstruction methods can carry over. Specifically, we show how to use voltage and GPS information to infer associations within one transformer area, i.e., to identify the meter-transformer connectivity. To give a guarantee, we show a theoretic bound for our clustering method, providing the ability to explain the performance of the machine learning method. The proposed algorithm has been validated by IEEE test systems and Duquesne Light Company in Pittsburgh, showing outstanding performance. A utility implementation is also demonstrated.
AB - The ongoing deployment of Distributed Energy Resources, while bringing benefits, introduces significant challenges to the electric utility industry, especially in the distribution grid. These challenges call for closer monitoring through state estimation, where real-time topology recovery is the basis for accurate modeling. Previous methods either ignore geographical information, which is important in connectivity identification or are based on an ideal assumption of an isolated sub-network for topology recovery, e.g., within one transformer. This requires field engineers to identify the association, which is costly and may contain errors. To solve these problems, we propose a density-based topology clustering method that leverages both voltage domain data and the geographical space information to segment datasets from a large utility customer pool, after which other topology reconstruction methods can carry over. Specifically, we show how to use voltage and GPS information to infer associations within one transformer area, i.e., to identify the meter-transformer connectivity. To give a guarantee, we show a theoretic bound for our clustering method, providing the ability to explain the performance of the machine learning method. The proposed algorithm has been validated by IEEE test systems and Duquesne Light Company in Pittsburgh, showing outstanding performance. A utility implementation is also demonstrated.
KW - Density-based clustering
KW - Meter-transformer mapping
KW - Topology clustering
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U2 - 10.1016/j.ijepes.2022.108291
DO - 10.1016/j.ijepes.2022.108291
M3 - Article
AN - SCOPUS:85130630920
SN - 0142-0615
VL - 142
JO - International Journal of Electrical Power and Energy Systems
JF - International Journal of Electrical Power and Energy Systems
M1 - 108291
ER -