Federated-Learning-Based Distributed Frequency Control Against False Data Injection Attack

This article proposes the federated-learning-based second-order recurrent neural network (SORNN-FL) for distributed frequency control of a multimicrogrid (MMG) system. The problem is conceptualized as proportional-integral derivative consensus control using the Lyapunov function, which depicts the behavior of recurrent neural network (RNN). The parameters of the controller are obtained by deriving the dynamics of energy function to update the state of neurons. During the learning process, FL of weights is adopted to improve the performance of the network by increasing the communication among the neurons. The effectiveness of the developed SORNN-FL controller is tested for the MMG system, and its performance is compared with those of the MMG system using SORNN, RNN, and the conventional method of tuning. The results obtained reveal that the proposed higher order control gives an improved performance during uncertainties and communication failure. Specifically, we use a differential privacy mechanism to secure the data from false data injection attacks by the invaders. The various extensive cases studied on the MMG system demonstrate that the proposed privacy-preserving-based SORNN-FL scheme is robust and efficient. Even if a communication failure and a malicious attack occur in the MMG system, our proposed control scheme can regulate the system frequency and maintain system stability. The real-time validation has been demonstrated at the 0.4-kV microgrid test-bed system in the laboratory.

Automated summary

This article proposes a federated-learning-based second-order recurrent neural network (SORNN-FL) controller for distributed frequency control in a multimicrogrid system. The controller improves network performance through weight updates and protects data security using differential privacy mechanism. Extensive research and experiments demonstrate that the controller is robust and efficient.