Optimalization Droop Control Based on Aquila Optimizer Algorithm For DC Microgrid

Inconsistent circuit parameters and line impedances in DC microgrid impact various droop response characteristics. This causes the current sharing accuracy of each converter to decrease. A control response will cause DC voltage deviation. DC microgrids with multiple parallel-connected sources require well-established controls to maintain efficient system performance and prevent overloads. This study presents a droop control based on the Aquila optimization algorithm The AO duplicates the existence of Aquila birds in the realm while hunting prey. The performance of AO is comparing the transient response with conventional methods and particle swarm optimization. This study uses the case of load sharing and load changes for testing. Matlab software is used for simulation testing. From the simulation results, it was found that the AO method has the best ability. The AO is competent to alleviate the settling time by 0.7% compared to the conventional method and has better final power than the conventional method by 0.798%.