Distribution Systems AC State Estimation via Sparse AMI Data Using Graph Signal Processing

Shammya Shananda Saha, Anna Scaglione, Raksha Ramakrishna, Nathan G. Johnson

Research output: Contribution to journalArticlepeer-review

Abstract

This work establishes and validates a Grid Graph Signal Processing (G-GSP) framework for estimating the state vector of a radial distribution feeder. One of the key insights from GSP is the generalization of Shannon's sampling theorem for signals defined over the irregular support of a graph, such as the power grid. Using a GSP interpretation of Ohm's law, we show that the system state can be well approximated with relatively few components that correspond to low-pass Graph Fourier Transform (GFT) frequencies. The target application of this theory is the formulation of a three-phase unbalanced Distribution System State Estimation (DSSE) formulation that recovers the GFT approximation of the system state vector from sparse Advanced Metering Infrastructure (AMI) measurements. To ensure convergence of G-GSP for DSSE, the proposed solution relies on a convex relaxation technique. Furthermore, we propose an optimal sensor placement algorithm for AMI measurements. Numerical results demonstrate the efficacy of the proposed method.

Original languageEnglish (US)
Pages (from-to)3636-3649
Number of pages14
JournalIEEE Transactions on Smart Grid
Volume13
Issue number5
DOIs
StatePublished - Sep 1 2022
Externally publishedYes

Keywords

  • AC state estimation
  • advanced metering infrastructure
  • convex relaxation
  • graph signal processing
  • optimal sensor placement
  • radial distribution systems

ASJC Scopus subject areas

  • Computer Science(all)

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