Impacts of Bad Data on the PMU based Line Outage Detection
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Feb 2015.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 1.
5 papers.
- 2024A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunitiescited by 4
- 2022A Stream Learning Approach for Real-Time Identification of False Data Injection Attacks in Cyber-Physical Power Systemscited by 3
- 2021Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Gridscited by 2
- 2024An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Gridscited by 1
- 2025A Single-Point Measurement Framework for Robust Cyber-Attack Diagnosis in Smart Microgrids Using Dual Fractional-Order Feature Analysiscited by 0
- 2025A Single-Point Measurement Framework for Robust Cyber-Attack Diagnosis in Smart Microgrids Using Dual Fractional-Order Feature Analysis9 Jul 2025
- 2024A Comprehensive Survey on the Security of Smart Grid: Challenges, Mitigations, and Future Research Opportunities10 Jul 2024
- 2024An Unsupervised Adversarial Autoencoder for Cyber Attack Detection in Power Distribution Grids31 Mar 2024
- 2022A Stream Learning Approach for Real-Time Identification of False Data Injection Attacks in Cyber-Physical Power Systems13 Oct 2022
- 2021Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids17 Feb 2021