Adaptive Fixed-Time Consensus Protocol of a DC Microgrids Cluster

Abstract

This article introduces the Adaptive Fixed-Time Consensus algorithm (AFTCA) using a saturation function to address challenges arising from the constant parameter values in different operational conditions. To enhance the cluster's adaptability during emergencies, an Artificial Neural Networks (ANN) technique is leveraged to predict these parameters. Implementing this technique in the secondary control layer improves both cluster convergence and bus voltage regulation. The proposed control method outperforms existing approaches in the literature, achieving swift current convergence and enhanced voltage recovery across diverse operating conditions. The performance of the proposed control method is evaluated by simulating a cluster of four DC microgrids (MGs) using MATLAB.