Topology Estimation Using Graphical Models in Multi-Phase Power Distribution Grids

Deepjyoti Deka, Michael Chertkov, Scott Backhaus

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Power distribution grids are structurally operated radially, such that energized lines form a collection of trees with a substation at the root of each tree. The operational topology may change from time to time; however, tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This paper develops a learning framework to reconstruct the radial operational structure of the distribution grid from synchronized voltage measurements. To detect operational lines, our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions, and in particular Gaussian, for injections. We validate the algorithm through extensive experiments on ac three-phase IEEE distribution grid test cases.

Original languageEnglish (US)
Article number8632741
Pages (from-to)1663-1673
Number of pages11
JournalIEEE Transactions on Power Systems
Volume35
Issue number3
DOIs
StatePublished - May 2020
Externally publishedYes

Keywords

  • Distribution networks
  • computational complexity
  • conditional independence
  • graphical models
  • unbalanced three-phase

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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