Joint Estimation of Topology and Injection Statistics in Distribution Grids with Missing Nodes

Deepjyoti Deka, Michael Chertkov, Scott Backhaus

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Optimal operation of distribution grid resources relies on accurate estimation of its state and topology. Practical estimation of such quantities is complicated by the limited presence of real-time meters. This article discusses a theoretical framework to jointly estimate the operational topology and statistics of injections in radial distribution grids under limited availability of nodal voltage measurements. In particular, we show that our proposed algorithms are able to provably learn the exact grid topology and injection statistics at all unobserved nodes as long as they are not adjacent. The algorithm design is based on novel ordered trends in voltage magnitude fluctuations at node groups, that are independently of interest for radial physical flow networks. The complexity of the designed algorithms is theoretically analyzed and their performance is validated using both linearized and nonlinear ac power flow samples in test distribution grids.

Original languageEnglish (US)
Article number9019629
Pages (from-to)1391-1403
Number of pages13
JournalIEEE Transactions on Control of Network Systems
Volume7
Issue number3
DOIs
StatePublished - Sep 2020

Keywords

  • Clustering
  • complexity
  • distribution grid
  • linear flows
  • load estimation
  • missing nodes
  • spanning tree

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Control and Optimization

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