Grid-Graph Signal Processing (Grid-GSP): A Graph Signal Processing Framework for the Power Grid
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Abstract (arXiv)
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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Mar 2021.
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Citing papers per year: 2021: 1; 2022: 5; 2023: 2; 2024: 2; 2025: 1; 2026: 1.
12 papers.
- 2022Graph Filters for Signal Processing and Machine Learning on Graphscited by 53
- 2022Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutionscited by 28
- 2023GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filteringcited by 10
- 2022A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Gridscited by 7
- 2021Graph Neural Networks for Learning Real-Time Prices in Electricity Marketcited by 5
- 2022Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AIcited by 4
- 2024Multilinear Kernel Regression and Imputation via Manifold Learningcited by 3
- 2024Network Topology Inference from Smooth Signals Under Partial Observabilitycited by 2
- 2023Online Network Source Optimization with Graph-Kernel MABcited by 1
- 2026Stability of Flow Models for Graph Signalscited by 1
- 2022Graph Filter Transfer via Probability Density Ratio Weightingcited by 0
- 2025Cramer-Rao Bounds for Laplacian Matrix Estimationcited by 0
- 2026Stability of Flow Models for Graph Signals8 Jul 2026
- 2025Cramer-Rao Bounds for Laplacian Matrix Estimation6 Apr 2025
- 2024Network Topology Inference from Smooth Signals Under Partial Observability8 Oct 2024
- 2024Multilinear Kernel Regression and Imputation via Manifold Learning6 Feb 2024
- 2023GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filtering28 Nov 2023
- 2023Online Network Source Optimization with Graph-Kernel MAB7 Jul 2023
- 2022A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids7 Dec 2022
- 2022Graph Filters for Signal Processing and Machine Learning on Graphs16 Nov 2022
- 2022Graph Filter Transfer via Probability Density Ratio Weighting26 Oct 2022
- 2022Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI17 Aug 2022
- 2022Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutions16 May 2022
- 2021Graph Neural Networks for Learning Real-Time Prices in Electricity Market19 Jun 2021