Decentralized Low-Rank State Estimation for Power Distribution Systems

April Sagan, Yajing Liu, Andrey Bernstein

Research output: Contribution to journalArticlepeer-review

14 Scopus Citations

Abstract

This article 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 computationally challenging 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, 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.

Original languageAmerican English
Article number9352012
Pages (from-to)3097-3106
Number of pages10
JournalIEEE Transactions on Smart Grid
Volume12
Issue number4
DOIs
StatePublished - Jul 2021

Bibliographical note

Publisher Copyright:
© 2010-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-79070

Keywords

  • Distributed optimization
  • matrix completion
  • state estimation

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