A type-3 fuzzy control for current sharing and voltage balancing in microgrids

This paper studies the current sharing and voltage balancing problems of direct current microgrids (DC-MGs) consisting of distributed generation units (DGUs) connected by a communication network. The main challenge is that the DC-MG model is prone to unknown dynamic models and external disturbances. Moreover, the voltage at each DGU's point of coupling (PC) has to converge to its desired value while the information of the DGU filter current is exchanged with the nearest neighbors. To this end, the suggested distributed control algorithm benefits from an interval type-3 fuzzy logic system (IT3FLS). To enhance the accuracy of the approximation, a learning strategy is designed based on a correntropy unscented Kalman filter (CUKF) with a fuzzy kernel size. Utilizing the approximation technique and merging the consensus-based secondary control policy with the proposed type-3 fuzzy (T3F) controller result in the balanced voltages of the closed-loop DC-MG. The convergence of the trajectories of the DC-MG is ensured and the effects of approximation error signals are investigated via the proposed method. Furthermore, the robustness of the voltages and currents against unknown uncertainties admits the efficiency of the suggested learning-based control policy. The simulation results also confirm the appropriate transient response and the robustness of trajectories, thus the suggested controller can be implemented for practical cases. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper addresses the issues of current sharing and voltage balancing in direct current microgrids. A distributed control algorithm based on interval type-3 fuzzy logic system (IT3FLS) is proposed to overcome the challenges caused by unknown dynamic models and external disturbances. A learning strategy utilizing correntropy unscented Kalman filter (CUKF) with fuzzy kernel size is designed to improve the accuracy of approximation. The suggested control policy ensures convergence of the microgrid trajectories and robustness against uncertainties, as demonstrated by simulation results. The proposed controller shows promising performance for practical applications.