Multi-Area Model-Free State Estimation via Distributed Tensor Decomposition

Yajing Liu, Ahmed Zamzam, Andrey Bernstein

Research output: Contribution to conferencePaperpeer-review

1 Scopus Citations

Abstract

This paper proposes a model-free method for distribution system state estimation based on tensor completion using canonical polyadic decomposition. In particular, we consider a setting where the network is divided into multiple areas. The measured physical quantities at buses located in the same area are processed by an area controller. A third-order tensor is constructed to collect these measured quantities. The measurements are analyzed locally to recover the full state information of the network. A closed-form iterative algorithm based on the alternating direction method of multipliers is developed to obtain the low-rank factors of the whole network state tensor where information exchange happens only between neighboring areas. To demonstrate the efficacy of the developed algorithm, numerical simulations are carried out using an IEEE test system.

Original languageAmerican English
Pages191-195
Number of pages5
DOIs
StatePublished - 1 Nov 2020
Event54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States
Duration: 1 Nov 20205 Nov 2020

Conference

Conference54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/TerritoryUnited States
CityPacific Grove
Period1/11/205/11/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

NREL Publication Number

  • NREL/CP-5D00-78539

Keywords

  • alternating direction method of multipliers
  • canonical polyadic decomposition
  • distribution system state estimation
  • Tensor completion

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