Decentralized Low-Rank State Estimation for Power Distribution Systems

TL;DR

This paper introduces a decentralized matrix completion-based state estimation algorithm for power distribution systems that enhances scalability and privacy by distributing computations across network areas using proximal ADMM.

Contribution

It develops a scalable decentralized algorithm for low-rank state estimation in power networks, eliminating the need for centralized data processing.

Findings

Effective in low-observability scenarios

Scalable and distributed computation

Validated on IEEE test cases

Abstract

This paper considers the low-observability state estimation problem in power distribution networks and develops a decentralized state estimation algorithm leveraging the matrix completion methodology. Matrix completion has been shown to be an effective technique in state estimation that exploits the low dimensionality of the power system measurements to recover missing information. This technique can utilize an approximate (linear) load flow model, or it can be used with no physical models in a network where no information about the topology or line admittance is available. The direct application of matrix completion algorithms requires solving a semi-definite programming (SDP) problem, which becomes infeasible for large networks. We therefore develop a decentralized algorithm that capitalizes on the popular proximal alternating direction method of multipliers (proximal ADMM). The method allows us to distribute the computation among different areas of the network, thus leading to a scalable algorithm. By doing all computations at individual control areas and only communicating with neighboring areas, the algorithm eliminates the need for data to be sent to a central processing unit and thus increases efficiency and contributes to the goal of autonomous control of distribution networks. We illustrate the advantages of the proposed algorithm numerically using standard IEEE test cases.