Research on Microgrid Frequency Defense Against FDI Attacks Based on Artificial Neural Networks

Microgrid systems with open communication networks are vulnerable to false data injection (FDI) attacks. Nodes that are attacked on transmission lines can affect the operational safety of the next receiving node, thereby endangering the operational security of the entire microgrid system. This paper proposes a dynamic adjustment method based on artificial neural networks. By learning historical secondary control rated operating parameter settings and state information, it completes the training of intelligent prediction and decision models to adaptively adjust the coefficients of secondary distributed control, thereby achieving resistance against FDI attacks and maintaining stable frequency operation of the entire system. Simulation results verify the feasibility and effectiveness of the proposed method.