Monitoring and Optimization for Power Grids: A Signal Processing Perspective
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Abstract (arXiv)
The smart grid vision is to revitalize the electric power network by leveraging the proven sensing, communication, control, and machine learning technologies to address pressing issues related to security, stability, environmental impact, market diversity, and novel power technologies. Significant effort and investment have been committed to architect the necessary infrastructure by installing advanced metering systems and establishing data communication networks throughout the grid. Signal processing methodologies are expected to play a major role in this context by providing intelligent algorithms that fully exploit such pervasive sensing and control capabilities to realize the vision and manifold anticipated benefits of the smart grid. In this feature article, analytical background and relevance of signal processing tools to power systems are delineated, while introducing major challenges and opportunities for the future grid engineering. From grid informatics to inference for monitoring and optimization tools, energy-related issues are shown to offer a fertile ground for signal processing growth whose time has come.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Feb 2013.
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Citing papers per year: 2021: 5; 2022: 1; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
9 papers.
- 2021Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Gridscited by 24
- 2021Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networkscited by 15
- 2023GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filteringcited by 10
- 2021Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networkscited by 8
- 2021Embedding Power Flow into Machine Learning for Parameter and State Estimationcited by 6
- 2022Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approachcited by 4
- 2021Detection of Insider Attacks in Distributed Projected Subgradient Algorithmscited by 0
- 2025Cramer-Rao Bounds for Laplacian Matrix Estimationcited by 0
- 2026A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitmentcited by 0
- 2026A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment23 Apr 2026
- 2025Cramer-Rao Bounds for Laplacian Matrix Estimation6 Apr 2025
- 2023GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filtering28 Nov 2023
- 2022Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approach11 Feb 2022
- 2021Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks25 Dec 2021
- 2021Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks24 Apr 2021
- 2021Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids5 Apr 2021
- 2021Embedding Power Flow into Machine Learning for Parameter and State Estimation26 Mar 2021
- 2021Detection of Insider Attacks in Distributed Projected Subgradient Algorithms18 Jan 2021