Joint Estimation of Topology and Injection Statistics in Distribution Grids with Missing Nodes

TL;DR

This paper presents a theoretical framework and algorithms for jointly estimating the topology and injection statistics of radial distribution grids with limited voltage measurements, ensuring accurate grid state estimation even with missing data.

Contribution

It introduces provably effective algorithms that learn grid topology and injection statistics at unobserved nodes, based on voltage fluctuation trends, applicable to both linearized and non-linear power flow models.

Findings

Algorithms accurately learn grid topology with missing data.

Performance validated on linearized and non-linear power flow models.

Theoretical analysis confirms algorithm complexity and effectiveness.

Abstract

Optimal operation of distribution grid resources relies on accurate estimation of its state and topology. Practical estimation of such quantities is complicated by the limited presence of real-time meters. This paper discusses a theoretical framework to jointly estimate the operational topology and statistics of injections in radial distribution grids under limited availability of nodal voltage measurements. In particular we show that our proposed algorithms are able to provably learn the exact grid topology and injection statistics at all unobserved nodes as long as they are not adjacent. The algorithm design is based on novel ordered trends in voltage magnitude fluctuations at node groups, that are independently of interest for radial physical flow networks. The complexity of the designed algorithms is theoretically analyzed and their performance validated using both linearized and non-linear AC power flow samples in test distribution grids.