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Detection and Mitigation of Cyber Attacks on Voltage Stability Monitoring of Smart Grids

This paper investigates the possible cyber-physical attacks on voltage stability monitoring of a power transmission system. By considering a smart adversary that can launch a vector attack based on power flow equations, conventional voltage stability monitoring systems based on voltage stability indexes will fail to detect the instability issue. This can mislead the Power System Operator (PSO) to take inappropriate corrective action. In order to prevent and to combat such a malicious attack, a new indicator for efficient intrusion detection and a novel mitigation algorithm is proposed. This proposed detection algorithm employs the multi-port equivalent circuit of the power system to calculate the Thevenin Equivalent (TE) parameters. These TE parameters are then used to calculate an indicator, which reveals the attack on Phasor Measurement Unit (PMU) data. The proposed mitigation scheme then helps PSO to detect which PMU is under-attack. Furthermore, the amount of injected attack vectors is computed or estimated depending on the number of compromised PMUs. The effectiveness of the proposed techniques is demonstrated via illustrative case studies.
- University of Chicago United States
- Hydro-Québec Canada
- Hydro-Québec Canada
- Concordia University Canada
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).36 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
