Distributed Event-Triggered Learning-Based Control for Battery Energy Storage Systems Under Persistent False Data Injection Attacks
Ying Wan, Guanghui Wen, Xinghuo Yu, Jürgen Kurths, Zhiyi Chen
Abstract
This paper aims to address distributed event-triggered learning-based secure control for multiple battery energy storage systems (BESSs) under persistent false-date injection (FDI) attacks. To tackle FDI attacks and also save communication resources, a distributed learning-based secure control based on a dynamic event-triggered framework is established. This control scheme uses an adaptive law to update the estimation matrix for a neural network (NN) approximator, using only relative state variables at triggered instants. To ensure uniform boundedness of all variables involved in the update law, a proper projection operator is introduced. Additionally, the updated law incorporates a low-pass filter structure, which can suppress unfavorable high-frequency oscillations when a high-gain learning rate is applied. It is rigorously proven that under such distributed event-triggered learning-based control protocols, frequency regulation, active power sharing, and SoC balancing can be achieved with arbitrary accuracy by adjusting the learning rates and control parameters. Finally, real-time simulations of the IEEE 57-bus system are performed using OPAL-RT to illustrate the efficacy of the developed learning strategy.