CYBER ATTACKS AGAINST STATE ESTIMATION IN POWER SYSTEMS: VUNERABILITY ANALYSIS AND PROTECTION STRATEGIES

Power grid is one of the most critical infrastructures in a nation and could suffer a variety of cyber attacks. With the development of Smart Grid, cyber security has become an area of growing concern. False data injection attack has recently attracted wide research interest. This thesis proposes a false data attack model with incomplete network information and develops optimal attack strategies for attacking load measurements and the real-time topology of a power grid. The impacts of false data on the economic and reliable operations of power systems are quantitatively analyzed in this thesis. To mitigate the risk of cyber attacks, a distributed protection strategies are also developed. It has been shown that an attacker can design false data to avoid being detected by the control center if the network information of a power grid is known to the attacker. In practice, however, it is very hard or even impossible for an attacker to obtain all network information of a power grid. In this thesis, we propose a local load redistribution attacking model based on incomplete network information and show that an attacker only needs to obtain the network information of the local attacking region to inject false data into smart meters in the local region without being detected by the state estimator. A heuristic algorithm is developed to determine a feasible attacking region by obtaining reduced network information. This thesis investigates the impacts of false data on the operations of power systems. It has been shown that false data can be designed by an attacker to: 1) mask the real-time topology of a power grid; 2) overload a transmission line; 3) disturb the line outage detection based on PMU data. To mitigate the risk of cyber attacks, this thesis proposes a new protection strategy, which intends to mitigate the most damaging effect of LR attacks on power system operation. The objective is to mitigate the damage effects of false data injection attacks by increasing the attacking cost of an attacker. This is achieved by protecting a small set of critical measurements. To further reduce the computation complexity, we also propose a mixed integer linear programming approach to separate the power grid into several subnetworks, then distributed protection strategy is applied to each subnetwork. The results of this thesis reveal the mechanism of local false data injection attacks and highlight the importance and complexity of defending power systems against false data injection attacks.Ph.D. in Electrical and Computer Engineering, July 201