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Limited presence of nodal and line meters in distribution grids hinders theiroptimal operation and participation in real-time markets. In particular lack ofreal-time information on the grid topology and infrequently calibrated lineparameters (impedances) adversely affect the accuracy of any operational powerflow control. This paper suggests a novel algorithm for learning the topologyof distribution grid and estimating impedances of the operational lines withminimal observational requirements - it provably reconstructs topology andimpedances using voltage and injection measured only at the terminal (end-user)nodes of the distribution grid. All other (intermediate) nodes in the networkmay be unobserved/hidden. Furthermore no additional input (e.g., number of gridnodes, historical information on injections at hidden nodes) is needed for thelearning to succeed. Performance of the algorithm is illustrated in numericalexperiments on the IEEE and custom power distribution models.

Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability