As microgrids become increasingly interconnected through high-voltage DC systems, they face growing cybersecurity vulnerabilities. False data injection attacks—where malicious actors introduce fabricated measurements into control systems—pose a significant threat to grid stability and reliability. This research addresses that challenge by presenting a comprehensive defense mechanism for AC microgrids connected via flexible DC networks.
The proposed approach begins by modeling how false data injection attacks propagate through distributed control algorithms used in AC/DC converters. These converters are critical interfaces that manage power flow between AC microgrids and the connecting DC backbone. When attacked, corrupted state information can cause voltage sags, frequency deviations, and loss of synchronization among otherwise stable microgrids.
The defense strategy employs a distributed secondary control framework based on consensus theory—a proven technique where multiple autonomous controllers share information and coordinate responses. Crucially, each controller incorporates an adaptive compensation mechanism that identifies when received data deviates from expected patterns, then actively corrects distorted measurements before they propagate through the network.
The system operates at two levels: primary control maintains local stability at each converter, while the secondary layer coordinates voltage and frequency restoration across the entire interconnected system. This two-tier design ensures that even if some control nodes receive corrupted information, the distributed consensus process allows healthy nodes to isolate and override malicious signals.
Simulation testing on a multi-terminal AC system demonstrated the strategy's effectiveness. Under various FDIA scenarios—including random and bounded attack signals—the control system maintained synchronization, suppressed voltage and frequency deviations, and prevented disturbances from cascading across regions. Recovery times were rapid, with stable operating conditions restored in real time.
These results highlight an important advancement for grid operators managing complex, geographically dispersed microgrids. As cyber threats to energy infrastructure intensify, resilient control architectures that combine detection, isolation, and adaptive correction offer a practical pathway toward more secure and reliable interconnected power systems.
