Detection and Defense Method Against False Data Injection Attacks for Distributed Load Frequency Control System in Microgrid

In the realm of microgrid (MG), the distributed load frequency control (LFC) system has proven to be highly susceptible to the negative effects of false data injection attacks (FDIAs). Considering the significant responsibility of the distributed LFC system for maintaining frequency stability within the MG, this paper proposes a detection and defense model against unobservable FDIAs in the LFC system. Firstly, the model integrate a Bi-directional long short-term memory (BiLSTM) neural network and an improved whale optimization algorithm (IWOA) into the LFC controller to detect and counteract FDIAs. Secondly, to enable the BiLSTM neural network to proficiently detect multiple types of FDIAs with utmost precision, the model employs a historical MG dataset comprising the frequency and power variances. Finally, the IWOA is utilized to optimize the proportional-integral-derivative (PID) controller parameters to counteract the negative impacts of FDIAs. The proposed detection and defense model is validated by building the distributed LFC system in Simulink.