Estimating distribution grid topologies: A graphical learning based approach

Deepjyoti Deka, Scott Backhaus, Michael Chertkov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

51 Scopus citations


Distribution grids represent the final tier in electric networks consisting of medium and low voltage lines that connect the distribution substations to the end-users/loads. Traditionally, distribution networks have been operated in a radial topology that may be changed from time to time. Due to absence of a significant number of real-time line monitoring devices in the distribution grid, estimation of the topology/structure is a problem critical for its observability and control. This paper develops a novel graphical learning based approach to estimate the radial operational grid structure using voltage measurements collected from the grid loads. The learning algorithm is based on conditional independence tests for continuous variables over chordal graphs and has wide applicability. It is proven that the scheme can be used for several power flow laws (DC or AC approximations) and more importantly is independent of the specific probability distribution controlling individual bus's power usage. The complexity of the algorithm is discussed and its performance is demonstrated by simulations on distribution test cases.

Original languageEnglish (US)
Title of host publication19th Power Systems Computation Conference, PSCC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9788894105124
StatePublished - Aug 10 2016
Externally publishedYes
Event19th Power Systems Computation Conference, PSCC 2016 - Genova, Italy
Duration: Jun 20 2016Jun 24 2016

Publication series

Name19th Power Systems Computation Conference, PSCC 2016


Conference19th Power Systems Computation Conference, PSCC 2016


  • Computational Complexity
  • Conditional Independence
  • Distribution Networks
  • Graphical Models
  • Power Flows

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology


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