Event–triggered neural network voltage control for distribution networks under actuator attacks based on observers

This paper proposes an event-triggered neural network control approach to address the voltage control issue in distribution networks under false data injection (FDI) attacks. Firstly, a mathematical model of voltage deviation in distribution networks considering the impact of FDI attacks is established to accurately represent the dynamic behavior of the attacked system. To optimize the utilization of communication resources within the network, an adaptive event-triggered mechanism is designed, which can dynamically adjust the triggering conditions based on the system state, effectively reducing unnecessary communication instances. On this basis, an event-triggered voltage control (VC) system model is established. To effectively mitigate voltage over limit caused by FDI attacks, an adaptive neural network controller is designed, which can compensate for the attack signals and keep the voltage within the allowable range. By combining Lyapunov–Krasovskii stability theory with linear matrix inequality (LMI) techniques, the stability of the system is analyzed, and sufficient conditions for ensuring it are derived. Finally, simulation results demonstrate that this method can not only effectively resist FDI attacks, but also significantly reduce the communication burden while ensuring system stability.