Network-Aware Control Barrier Functions for Resilient Microgrids Under Stealthy Drift Attacks

Area of Science:

• Electrical Engineering
• Control Systems
• Cybersecurity

Background:

• Inverter-dominated microgrids face significant risks from stealthy cyber-physical drift attacks.
• These attacks, characterized by low-amplitude, slow perturbations, can degrade voltage and cause grid collapse, bypassing traditional security measures.

Purpose of the Study:

• To introduce a resilient, delay-aware supervisory control architecture for microgrids.
• To act as an online safety shield at the actuator interface, mitigating cyber-physical drift attacks.

Main Methods:

• Physics-informed Control Barrier Functions (CBFs) were constructed by modeling nonlinear power-flow and communication topologies.
• Structural electrical invariants from the nodal admittance matrix (Ybus) were embedded into the CBFs.
• Heterogeneous, time-varying network delays were incorporated into safety constraints, with a threat-adaptive modulation loop.

Main Results:

• The closed-loop tracking error was proven to be Input-to-State Stable (ISS) under drift attacks and network latencies using a Lyapunov-Krasovskii functional.
• Simulations on an IEEE 14-bus test feeder showed consistent enforcement of safety margins and reduced voltage violations.
• The architecture effectively bounded system trajectories and prevented voltage collapse under coordinated sub-threshold attacks.

Conclusions:

• The developed architecture establishes a scalable, cross-layer safety framework for resilient distribution systems.
• It offers enhanced protection against sophisticated cyber-physical attacks in inverter-dominated microgrids.
• The approach demonstrates effectiveness in maintaining grid stability and preventing voltage collapse under adversarial conditions.