Biblio

In recent times, an increasing amount of intelligent electronic devices (IEDs) are being deployed to make power systems more reliable and economical. While these technologies are necessary for realizing a cyber-physical infrastructure for future smart power grids, they also introduce new vulnerabilities in the grid to different cyber-attacks. Traditional methods such as state vector estimation (SVE) are not capable of identifying cyber-attacks while the geometric information is also injected as an attack vector. In this paper, a machine learning based smart grid attack identification method is proposed. The proposed method is carried out by first collecting smart grid power flow data for machine learning training purposes which is later used to classify the attacks. The performance of both the proposed SVM method and the traditional SVE method are validated on IEEE 14, 30, 39, 57 and 118 bus systems, and the performance regarding the scale of the power system is evaluated. The results show that the SVM-based method performs better than the SVE-based in attack identification over a much wider scale of power systems.

Real-time supervisory control and data acquisition (SCADA) systems use remote terminal units (RTUs) to monitor and manage the flow of power at electrical substations. As their connectivity to different utility and private networks increases, RTUs are becoming more vulnerable to cyber-attacks. Some attacks seek to access RTUs to directly control power system devices with the intent to shed load or cause equipment damage. Other attacks (such as denial-of-service) target network availability and seek to block, delay, or corrupt communications between the RTU and the control center. In the most severe case, when communications are entirely blocked, the loss of an RTU can cause the power system to become unobservable. It is important to understand how losing an RTU impacts the system state (bus voltage magnitudes and angles). The system state is determined by the state estimator and serves as the input to other critical EMS applications. There is currently no systematic approach for assessing the cyber-physical impact of losing RTUs. This paper proposes a methodology for N-1 RTU cyber-physical security assessment that could benefit power system control and operation. We demonstrate our approach on the IEEE 14-bus system as well as on a synthetic 200-bus system.

Intelligent connected vehicle system tightly integrates computing, communication, and control strategy. It can increase the traffic throughput, minimize the risk of accidents and reduce the energy consumption. However, because of the openness of the vehicular ad hoc network, the system is vulnerable to cyber-attacks and may result in disastrous consequences. Hence, it is interesting in design of the connected vehicular systems to be resilient to the sensor attacks. The paper focuses on the estimation and control of the intelligent connected vehicle systems when the sensors or the wireless channels of the system are attacked by attackers. We give the upper bound of the corrupted sensors that can be corrected and design the state estimator to reconstruct the initial state by designing a closed-loop controller. Finally, we verify the algorithm for the connected vehicle system by some classical simulations.