Biblio

In the past decade, cyber-attack events on the power grid have proven to be sophisticated and advanced. These attacks led to severe consequences on the grid operation, such as equipment damage or power outages. Hence, it is more critical than ever to develop tools for security assessment and detection of anomalies in the cyber-physical grid. For an extensive power grid, it is complex to analyze the causes of frequency deviations. Besides, if the system is compromised, attackers can leverage on the frequency deviation to bypass existing protection measures of the grid. This paper aims to develop a novel specification-based method to detect False Data Injection Attacks (FDIAs) in the multi-area system. Firstly, we describe the implementation of a three-area system model. Next, we assess the risk and devise several intrusion scenarios. Specifically, we inject false data into the frequency measurement and Automatic Generation Control (AGC) signals. We then develop a rule-based method to detect anomalies at the system-level. Our simulation results proves that the proposed algorithm can detect FDIAs in the system.