Mitigating False Data Injection Attacks on Inverter Set Points in a 100% Inverter-Based Microgrid
Milad Beikbabaei, Mario Montaño, Ali Mehrizi‐Sani, Chen‐Ching Liu
Abstract
The increasing number of 100% inverter-based microgrids is introducing new challenges in their control and cybersecurity. Previous work has studied the cyber vulnerabilities of microgrids; however, very few work has studied methods to mitigate and detect cyberattacks in a 100% inverter-based microgrid. Attackers can utilize communication-based devices in a microgrid to launch false data injection (FDI) attacks and cause voltage and frequency instability. This paper studies the effects of FDI attacks on the real and reactive power set points of inverter-based resources (IBR) in a 100% inverter-based microgrid. This work co-simulates a power system using PSCAD and a communication system using Python to study FDI attacks. The communication system is modeled as a first in first out (FIFO) queue model. A long short-term memory (LSTM)based method is used to mitigate and detect ramp and bias FDI attacks. The proposed strategy is tested on a microgrid with four IBRs subject to different FDI attacks.