Detection and Mitigation of Cyber-Attacks on AGC Systems of Low Inertia Power Grid

Abstract

Automatic generation control (AGC) plays a salient role in modern power systems by maintaining grid frequency within acceptable limits. Since AGC is the only automatic feedback loop between cyber and physical infrastructures, it is greatly vulnerable to malicious attacks and hence, required urgent investigations. Attackers can falsify the sensor measurements employed for AGC operation causing service outage and infrastructural damage. In this article, a method is developed to detect and mitigate possible cyber-attack schemes such as false data injection and denial-of-service attacks targeting the AGC systems. The proposed method is termed as cyber-attack detection and mitigation platform (CDMP) and utilizes the forecasted data of area control error for identification and mitigation of attacks. The CDMP strategy involves three stages for optimal operation and identifies any arbitrary false data injected to the grid. For the investigation, three area system with multiple generations and distribution companies under bilateral trading is considered. The test model is also equipped with inertia emulation technique supported by battery management system. It is because the modern power system is advancing toward inverter-dominated system from synchronous-based system due to high penetration of renewables. The effectiveness of the proposed strategy is verified using rigorous simulation results.