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Game-Theoretic Neural Consensus Controller for FDIA-Resilient DC Microgrids
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DOI：10.1109/jestpe.2026.3693111.png)
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Abstract
En 中文
The increasing integration of distributed generations (DGs) in modern power systems has led to the proliferation of direct current microgrids (DCMGs) due to their operational efficiency and reduced power conversion losses. However, their dependence on communication networks makes them susceptible to cyber threats, particularly false data injection attacks (FDIAs), which can compromise system stability and control performance. This article proposes a game-theoretic neural consensus (GTNC) controller to enhance the cybersecurity and operational stability of DCMGs. By leveraging potential game theory, the controller ensures consensus even in the presence of cyberattacks. A dynamic average consensus-based control algorithm is employed to maintain global voltage regulation and proportional power sharing among DGs. Furthermore, an artificial neural network (ANN)-based attack mitigation strategy is integrated to detect and neutralize FDIAs before they propagate through the system. The proposed methodology is validated through MATLAB/Simulink-based simulations and real-time hardware implementation using an OPAL-RT controller. The experimental results demonstrate that the proposed control strategy effectively preserves system stability, restores voltage regulation, and maintains consensus even in the presence of adversarial attacks. This research contributes to the development of cyber-resilient distributed control strategies for future DCMGs, enhancing their robustness, security, and reliability in smart power networks.
Keywords:
Artificial neural network (ANN)-based attack mitigation
direct current microgrid (DCMG)
false data injection attack (FDIA)
game theory
improved dynamic average consensus
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