Resilient Event Triggering-Based Agent Uncertainty Compensation in Grid-Forming Autonomous Network

Misbehaving agents in microgrids, caused by sensor faults, communication failures, malicious actions, or algorithmic deviations, can severely affect system stability and efficiency. This paper presents a dynamic event-triggered secondary control strategy, coordinated with primary control, to enhance performance and resilience. An event-based adaptive law for neural network (NN) observers is proposed to detect and mitigate agent deviations in distributed generation (DG) systems within an autonomous network. Event-triggering rules for updating NN weights and secondary control actions are derived to minimize communication and control efforts. A finite-time convergent primary control, based on dual sliding-surface formulations, complements the secondary layer by estimating power profile deviations under droop control during disturbances. Simulation results in MATLAB/Simulink confirm that the proposed framework improves stability and resiliency against agent deviations in leader-follower DG networks.