Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability

TL;DR

This paper introduces a novel algorithm that accurately reconstructs distribution grid topology and estimates line impedances using minimal measurements from only terminal nodes, even with hidden intermediate nodes, enhancing real-time grid management.

Contribution

The paper presents a new algorithm capable of learning grid topology and line impedances with minimal observational data, without requiring prior knowledge of hidden nodes or historical information.

Findings

Algorithm successfully reconstructs topology and estimates impedances in simulations.

Performs well with limited measurements from terminal nodes.

Validated on IEEE and custom distribution models.

Abstract

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.