Federated learning-driven distributed frequency control of interconnected microgrids using a fuzzy RNN–FOPID controller

Interconnected microgrid systems (IMSs) provide a strong foundation for improving the effectiveness of multiple distributed energy resources (DERs); however, operating numerous DERs in tandem remains a major obstacle. This paper presents a distributed control strategy (DCS) based on a federated learning (FL) fuzzy-optimised recurrent neural network (F-RNN), which uses an adaptive fractional-order proportional–integral–derivative (FOPID) controller to regulate frequency deviation. The control problem is formulated as a fractional-order consensus control strategy, using a Lyapunov-based framework that captures the dynamic behaviour of the RNN. Controller parameters are obtained from the dynamics of the energy function to update neuronal states, whereas FL is used during training to enhance network performance through improved information exchange. The proposed controller was demonstrated in Simulation/MATLAB and in real-time OPAL-RT Hardware-In-The-Loop under various conditions like load-demand uncertainty, stochastic loads, presence or absence of communication delay, communication link failure and source loss. Results based on quantitative analysis show that the proposed controller will be superior to the conventional controllers. More specifically, the proposed controller provides up to 73% reduction in integral absolute error, 33% lowering of the integral time absolute error, and greater than 70% improvement in integral time-weighted squared error as compared to the RNN–FOPID controller. The settling time has been improved from approximately 6.1 s down to 3.0 s with the peak frequency deviation being reduced by nearly 80%, illustrating the improved damping and transient performance of the proposed method.