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

TL;DR

This paper introduces Grid-GSP, a graph signal processing framework for power grids, modeling voltage data as low-pass graph signals to improve data sampling, inference, anomaly detection, and compression.

Contribution

It formalizes a low-pass graph filter model for voltage data in power systems, connecting physical grid dynamics with graph signal processing techniques.

Findings

Grid-GSP effectively models voltage data as low-dimensional subspaces.

The framework improves grid data sampling and interpolation.

Numerical results validate the approach on synthetic and real-world grid data.

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.