  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.
