# Monitoring and Optimization for Power Grids: A Signal Processing Perspective

arXiv: https://arxiv.org/abs/1302.0885 · HTML: https://mlsanity.com/paper/1302.0885/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2013.

## Built on by (most cited first, 9 of 9)

- [Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids](https://mlsanity.com/paper/2104.02012/) (2021)
- [Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks](https://mlsanity.com/paper/2104.11846/) (2021)
- [GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filtering](https://mlsanity.com/paper/2311.16602/) (2023)
- [Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks](https://mlsanity.com/paper/2112.13166/) (2021)
- [Embedding Power Flow into Machine Learning for Parameter and State Estimation](https://mlsanity.com/paper/2103.14251/) (2021)
- [Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approach](https://mlsanity.com/paper/2202.05498/) (2022)
- [Detection of Insider Attacks in Distributed Projected Subgradient Algorithms](https://mlsanity.com/paper/2101.06917/) (2021)
- [Cramer-Rao Bounds for Laplacian Matrix Estimation](https://mlsanity.com/paper/2504.04576/) (2025)
- [A Multi-Stage Warm-Start Deep Learning Framework for Unit Commitment](https://mlsanity.com/paper/2604.21891/) (2026)

## 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.