Estimating topology and injection statistics in distribution grids with hidden nodes

Deepjyoti Deka, Michael Chertkov, Scott Backhaus

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

1 Scopus citations

Abstract

Topology estimation is a critical problem in distribution grids that affect real-time control and optimization of grid operations. It is however hindered by the limited presence of real-time meters at the grid nodes and lines. This paper discusses a framework to learn the radial topology in distribution grids using limited nodal meters and no line meters. The most significant feature of the learning framework is that it is able to provably learn the topology as long as two hidden nodes (without meters) are not adjacency to one another in the operational grid. Our learning problem allows for greater number of hidden nodes than known prior work in this area. Further the algorithm does not require historical data for the hidden nodes and estimates their injection covariances. The learning algorithm uses ordered trends as well as equality constraints in nodal voltage fluctuations that arise from the radial topology. The efficiency of the designed algorithm is discussed by presenting simulation results for topology recovery in test radial grids.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages71-76
Number of pages6
ISBN (Electronic)9781538640555
DOIs
StatePublished - Apr 17 2018
Externally publishedYes
Event2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017 - Dresden, Germany
Duration: Oct 23 2017Oct 26 2017

Publication series

Name2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017
Volume2018-January

Conference

Conference2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017
Country/TerritoryGermany
CityDresden
Period10/23/1710/26/17

Keywords

  • Power Distribution networks
  • graphical models
  • hidden nodes
  • power flows
  • spanning tree
  • voltage measurements

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Safety, Risk, Reliability and Quality

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