Grid-Graph Signal Processing (Grid-GSP): A Graph Signal Processing Framework for the Power Grid

Raksha Ramakrishna, Anna Scaglione

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

The underlying theme of this paper is to explore the various facets of power systems data through the lens of graph signal processing (GSP), laying down the foundations of the Grid-GSP framework. Grid-GSP provides an interpretation for the spatio-temporal properties of voltage phasor measurements, by showing how the well-known power systems modeling supports a generative low-pass graph filter model for the state variables, namely the voltage phasors. Using the model we formalize the empirical observation that voltage phasor measurement data lie in a low-dimensional subspace and tie their spatio-temporal structure to generator voltage dynamics. The Grid-GSP generative model is then successfully employed to investigate the problems, pertaining to the grid, of data sampling and interpolation, network inference, detection of anomalies and data compression. Numerical results on a large synthetic grid that mimics the real-grid of the state of Texas, ACTIVSg2000, and on real-world measurements from ISO-New England verify the efficacy of applying Grid-GSP methods to electric grid data.

Original languageEnglish (US)
Article number9415125
Pages (from-to)2725-2739
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume69
DOIs
StatePublished - 2021

Keywords

  • Graph signal processing
  • PMU data compression
  • false data injection attack
  • network inference
  • optimal placement of PMU
  • phasor measurement units
  • sampling and recovery

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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