Optimal Real-time implementation of fuzzy logic control strategy for performance enhancement of autonomous microgrids

Distributed power grid systems require more intelligent and adaptable control management and optimization to preserve the best performance. These balance the generated power source and load demand even during severe interruptions. This research proposes a comparative analysis of an autonomous microgrid system optimized by fuzzy logic control and proportional-integral controller. The microgrid fuzzy logic control parameters are designed using the African vulture and Particle swarm optimization algorithms. At the same time, the proportional-integral controller is designed by using the Gorilla troops optimization algorithm. The suggested microgrid control techniques are verified using a MATLAB/Simulink environment. The overall system's detailed modeling and control methodologies are also presented. The investigation of the fuzzy logic control approach is also tested in a real-time setting. It is implemented by using OPAL real-time 4510 rapid control prototyping. The attained results prove the superiority of the fuzzy logic controller based on the African vulture optimization technique compared with other optimal control techniques. It is applied to give an ideal design for the fuzzy logic control parameters to preserve adequate power to the different loads. The proposed optimal controller is able to bring a robust and effective control with an accuracy of approximately 4%, 5%, 28%, and 26% than other techniques under different scenarios.

Automated summary

Distributed power grid systems need intelligent and adaptable control management to maintain optimal performance. This research compares an autonomous microgrid system controlled by fuzzy logic and proportional-integral controller, using optimization algorithms based on African vulture and gorilla troops. The proposed control techniques are validated in a MATLAB/Simulink environment.