Structure Learning and Statistical Estimation in Distribution Networks - Part II

TL;DR

This paper presents polynomial-time algorithms for learning the radial structure of distribution grids, estimating nodal consumption, and inferring line parameters from voltage measurements, even with partial data, validated through simulations.

Contribution

It introduces a unified approach combining structure learning, parameter estimation, and handling missing data in distribution networks using a Linear-Coupled approximation.

Findings

Algorithms accurately recover grid structure and parameters.

Effective even with limited or missing data.

Validated through simulations on multiple test cases.

Abstract

Part I of this paper discusses the problem of learning the operational structure of the grid from nodal voltage measurements. In this work (Part II), the learning of the operational radial structure is coupled with the problem of estimating nodal consumption statistics and inferring the line parameters in the grid. Based on a Linear-Coupled (LC) approximation of AC power flows equations, polynomial time algorithms are designed to complete these tasks using the available nodal complex voltage measurements. Then the structure learning algorithm is extended to cases with missing data, where available observations are limited to a fraction of the grid nodes. The efficacy of the presented algorithms are demonstrated through simulations on several distribution test cases.