# Impacts of Bad Data on the PMU based Line Outage Detection

arXiv: https://arxiv.org/abs/1502.04236 · HTML: https://mlsanity.com/paper/1502.04236/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2015.

## Built on by (most cited first, 5 of 5)

- [A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities](https://mlsanity.com/paper/2407.07966/) (2024)
- [A Stream Learning Approach for Real-Time Identification of False Data Injection Attacks in Cyber-Physical Power Systems](https://mlsanity.com/paper/2210.06729/) (2022)
- [Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids](https://mlsanity.com/paper/2102.09057/) (2021)
- [An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids](https://mlsanity.com/paper/2404.02923/) (2024)
- [A Single-Point Measurement Framework for Robust Cyber-Attack Diagnosis in Smart Microgrids Using Dual Fractional-Order Feature Analysis](https://mlsanity.com/paper/2507.06890/) (2025)

## Abstract (arXiv)

Power systems become more prone to cyber-attacks due to the high integration of information technologies. In this paper, we demonstrate that the outages of some lines can be masked by injecting false data into a set of measurements. The success of the topology attack can be guaranteed by making that: 1)the injected false data obeys KCL and KVL to avoid being detected by the bad data detection program in the state estimation; 2)the residual is increased such that the line outage cannot be detected by PMU data. A quadratic programming problem is set up to determine the optimal attack vector that can maximize the residual of the outaged line. The IEEE 39-bus system is used to demonstrate the masking scheme.