Three-Partition Multistrategy Adaptive Fruit Fly Optimization Algorithm for Microgrid Droop Control

Abstract

Summary. When the microgrid topology changes, the power output of the inverter cannot be adaptively adjusted by traditional droop control, and the dynamic performance and steady-state accuracy of the inverter are affected. To solve this problem, a three-partition multistrategy adaptive fruit fly optimization algorithm (MSAD-FOA) is proposed, which performs a real-time optimization of the PI parameters to realize microgrid droop control. The fruit fly population is divided into three regions according to the ranking of the fitness values of the algorithm. Next, the multistrategy model is automatically updated according to the difference in the fruit fly performance in each region. The local fine search in zone I ensures that the population does not degenerate. Zone II pertains to the adaptive adjustment to ensure the diversity and convergence of the algorithm. Zone III guides the fruit flies to accelerate convergence. The effectiveness of the algorithm and feasibility of the proposed control strategy are verified through a theoretical simulation and microgrid droop control simulation. The comparison with other algorithms demonstrates the superiority of the development and exploration ability of the proposed algorithm. The response speed of the inverter is 40 times higher when the proposed control strategy is used, and the steady-state error is reduced by 4.3%.