Sparse-Promoting Generative Adversarial Learning for Microgrid Control With Structural Adaptive Optimization Against FDI Attacks

With the rapid development of power electronics and renewable energy, microgrids have evolved into a flexible and complex networked control system. However, it also increases the likelihood of false data injection (FDI) attacks, which damages the performance of microgrids. To mitigate the negative impact of FDI attacks and enhance the reliability of microgrids, we optimize the control performance by addressing both the control system structure and the control laws. In this study, we propose a sparse-promoting generative adversarial learning scheme to simultaneously design the control structure and the control laws, without relying on a linearized analytical model. The proposed scheme estimates the attacked probability of each distributed generator (DG) nodes by conditional generative adversarial network (cGAN), while considering the impact of attacked nodes selection and adversarial detection on system performance. Subsequently, the solution of the optimal structure and control laws is conbined with the attack probability to realize the adaptive regulation against FDI attcks. In addition, we designed a security confidence function to ensure the maximum power output of attacked nodes under secure control when the control structure is optimized by sparse neural network (SNN). Simulations are conducted in MATLAB/Simulink to verify the effectiveness and advantages of the proposed scheme. The results demonstrate that the proposed scheme can effectively mitigate feeder power degradation during FDI attacks.