Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability
For power system operators, this method enables real-time grid monitoring and control with minimal sensor deployment, addressing a key bottleneck in distribution system management.
This paper proposes an algorithm that can exactly reconstruct the topology and estimate line impedances of a distribution grid using only voltage and injection measurements at terminal nodes, without requiring data from intermediate nodes or prior knowledge of the network size. Numerical experiments on IEEE and custom models demonstrate the algorithm's effectiveness.
Limited presence of nodal and line meters in distribution grids hinders their optimal operation and participation in real-time markets. In particular lack of real-time information on the grid topology and infrequently calibrated line parameters (impedances) adversely affect the accuracy of any operational power flow control. This paper suggests a novel algorithm for learning the topology of distribution grid and estimating impedances of the operational lines with minimal observational requirements - it provably reconstructs topology and impedances using voltage and injection measured only at the terminal (end-user) nodes of the distribution grid. All other (intermediate) nodes in the network may be unobserved/hidden. Furthermore no additional input (e.g., number of grid nodes, historical information on injections at hidden nodes) is needed for the learning to succeed. Performance of the algorithm is illustrated in numerical experiments on the IEEE and custom power distribution models.