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This paper discusses how a cyber attack could take advantage of torsional resonances in the shaft of turbo-generators to inflict severe physical damage to a power system. If attackers were able to take over the control of a battery energy storage device, they could modulate the injection of this device at a frequency that matches one of the sub-synchronous resonance frequencies of a generator. Small changes in injection might be sufficient to excite one of these mechanical resonances, resulting in metal fatigue and ultimately a catastrophic failure in the shaft of the generator. Using a state-space model of the electromechanical system, the paper develops transfer functions linking the magnitude of the malicious injections to the magnitude of oscillations in the speed and angle of the various masses connected to the shaft. Numerical results from a two-area power system demonstrate the existence of vulnerable frequencies and show that damaging mechanical oscillations can be triggered without causing easily detectable signals at the generator terminals. 

Voltage instability occurs when a power system is unable to meet reactive power demand at one or more buses. Voltage instability events have caused several major outages and promise to become more frequent due to increasing energy demand. The future smart grid may help to ensure voltage stability by enabling rapid detection of possible voltage instability and implementation of corrective action. These corrective actions will only be effective in restoring stability if they are chosen in a timely, scalable manner. Current techniques for selecting control actions, however, rely on exhaustive search, and hence may choose an inefficient control strategy. In this paper, we propose a submodular optimization approach to designing a control strategy to prevent voltage instability at one or more buses. Our key insight is that the deviation from the desired voltage is a supermodular function of the set of reactive power injections that are employed, leading to computationally efficient control algorithms with provable optimality guarantees. Furthermore, we show that the optimality bound of our approach can be improved from 1/3 to 1/2 when the power system operates under heavy loading conditions. We demonstrate our framework through extensive simulation study on the IEEE 30 bus test case.