Density-based clustering algorithm for associating transformers with smart meters via GPS-AMI data. (November 2022) - Cook, Elizabeth; Saleem, Muhammad Bilal; Weng, Yang; Abate, Stephen; Kelly-Pitou, Katrina; Grainger, Brandon
Abstract: 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. Highlights: DERs introduce challenges to the distribution grid. Such challenges call for meter-transformer mapping. Using voltage and GPS information to identify the meter-transformer connectivity. Theoretical guarantee for the robustness of the algorithm. Outstanding performance on IEEE test systems and a utility in Pittsburgh.