Learning partially observed meshed distribution grids

Abstract

This article analyzes statistical learning methods to identify the topology of meshed power distribution grids under partial observability. The learning algorithms use properties of the probability distribution of nodal voltages collected at the observed nodes. Unlike prior work on learning under partial observability, this work does not presume radial structure of the grid, and furthermore does not use injection measurements at any node. To the best of our knowledge, this is the first work for topology recovery in partially observed distribution grids, that uses voltage measurements alone. The developed learning algorithms are validated with non-linear power flow samples generated by Matpower in test grids.