Biblio

One major tool of Energy Management Systems for monitoring the status of the power grid is State Estimation (SE). Since the results of state estimation are used within the energy management system, the security of the power system state estimation tool is most important. The research in this area is targeting detection of False Data Injection attacks on measurements. Though this aspect is crucial, SE also depends on database that are used to describe the relationship between measurements and systems' states. This paper presents a two-stage optimization framework to not only detect, but also correct cyber-attacks pertaining the measurements' model parameters used by the SE routine. In the first stage, an estimate of the line parameters ratios are obtained. In the second stage, the estimated ratios from stage I are used in a Bi-Level model for obtaining a final estimate of the measurements' model parameters. Hence, the presented framework does not only unify the detection and correction in a single optimization run, but also provide a monitoring scheme for the SE database that is typically considered static. In addition, in the two stages, linear programming framework is preserved. For validation, the IEEE 118 bus system is used for implementation. The results illustrate the effectiveness of the proposed model for detecting attacks in the database used in the state estimation process.

Networked control systems improve the efficiency of cyber-physical plants both functionally, by the availability of data generated even in far-flung locations, and operationally, by the adoption of standard protocols. A side-effect, however, is that now the safety and stability of a local process and, in turn, of the entire plant are more vulnerable to malicious agents. Leveraging the communication infrastructure, the authors here present the design of networked control systems with built-in resilience. Specifically, the paper addresses attacks known as false data injections that originate within compromised sensors. In the proposed framework for closed-loop control, the feedback signal is constructed by weighted consensus of estimates of the process state gathered from other interconnected processes. Observers are introduced to generate the state estimates from the local data. Side-channel monitors are attached to each primary sensor in order to assess proper code execution. These monitors provide estimates of the trust assigned to each observer output and, more importantly, independent of it; these estimates serve as weights in the consensus algorithm. The authors tested the concept on a multi-sensor networked physical experiment with six primary sensors. The weighted consensus was demonstrated to yield a feedback signal within specified accuracy even if four of the six primary sensors were injecting false data.