TY - JOUR
T1 - Grid-Graph Signal Processing (Grid-GSP)
T2 - A Graph Signal Processing Framework for the Power Grid
AU - Ramakrishna, Raksha
AU - Scaglione, Anna
N1 - Funding Information:
Manuscript received May 30, 2020; revised November 7, 2020 and January 31, 2021; accepted April 3, 2021. Date of publication April 23, 2021; date of current version May 21, 2021. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Soummya Kar. This work was supported in part by the Director, Office of Electricity Delivery and Energy Reliability, Cybersecurity for Energy Delivery Systems program, of the U.S. Department of Energy, under Contract DOE0000780. Preliminary work was presented in [1] and [2]. (Corresponding author: Raksha Ramakrishna.) Raksha Ramakrishna is with the School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, Stockholm, Sweden (e-mail: rakshar@kth.se).
Publisher Copyright:
© 1991-2012 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Graph signal processing
KW - PMU data compression
KW - false data injection attack
KW - network inference
KW - optimal placement of PMU
KW - phasor measurement units
KW - sampling and recovery
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U2 - 10.1109/TSP.2021.3075145
DO - 10.1109/TSP.2021.3075145
M3 - Article
AN - SCOPUS:85104654849
VL - 69
SP - 2725
EP - 2739
JO - IRE Transactions on Audio
JF - IRE Transactions on Audio
SN - 1053-587X
M1 - 9415125
ER -