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Distribution grids include medium and low voltage lines that are involved inthe delivery of electricity from substation to end-users/loads. A distributiongrid is operated in a radial/tree-like structure, determined by switching on oroff lines from an underling loopy graph. Due to the presence of limitedreal-time measurements, the critical problem of fast estimation of the radialgrid structure is not straightforward. This paper presents a new learningalgorithm that uses measurements only at the terminal or leaf nodes in thedistribution grid to estimate its radial structure. The algorithm is based onresults involving voltages of node triplets that arise due to the radialstructure. The polynomial computational complexity of the algorithm ispresented along with a detailed analysis of its working. The most significantcontribution of the approach is that it is able to learn the structure incertain cases where available measurements are confined to only half of thenodes. This represents learning under minimum permissible observability.Performance of the proposed approach in learning structure is demonstrated byexperiments on test radial distribution grids.

Learning Topology of Distribution Grids using only Terminal Node Measurements