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

3 Scopus citations


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)1
Number of pages1
JournalIEEE Transactions on Smart Grid
StatePublished - Sep 1 2022


  • AC State Estimation
  • Advanced Metering Infrastructure
  • Convex Relaxation
  • Current measurement
  • Distribution networks
  • Graph Signal Processing
  • Optimal Sensor Placement.
  • Phasor measurement units
  • Radial Distribution Systems
  • Sparse matrices
  • Transformers
  • Transmission line measurements
  • Voltage measurement

ASJC Scopus subject areas

  • Computer Science(all)


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