Graph Algorithms for Topology Identification using Power Grid Probing

TL;DR

This paper introduces graph-based algorithms that leverage inverter probing to accurately identify power grid topology and line impedances, even with limited or noisy data, enhancing grid monitoring capabilities.

Contribution

It develops novel topology identification algorithms using inverter probing, providing recoverability guarantees under noisy conditions and limited measurement scenarios.

Findings

Complete feeder topology can be recovered with voltage data at all nodes.

Reduced feeder topology can be identified when voltage data are only at probing buses.

Proposed guidelines enable high-probability recoverability in noisy data conditions.

Abstract

To perform any meaningful optimization task, power distribution operators need to know the topology and line impedances of their electric networks. Nevertheless, distribution grids currently lack a comprehensive metering infrastructure. Although smart inverters are widely used for control purposes, they have been recently advocated as the means for an active data acquisition paradigm: Reading the voltage deviations induced by intentionally perturbing inverter injections, the system operator can potentially recover the electric grid topology. Adopting inverter probing for feeder processing, a suite of graph-based topology identification algorithms is developed here. If the grid is probed at all leaf nodes but voltage data are metered at all nodes, the entire feeder topology can be successfully recovered. When voltage data are collected only at probing buses, the operator can find a reduced feeder featuring key properties and similarities to the actual feeder. To handle modeling inaccuracies and load non-stationarity, noisy probing data need to be preprocessed. If the suggested guidelines on the magnitude and duration of probing are followed, the recoverability guarantees carry over from the noiseless to the noisy setup with high probability.