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%.
Highlights
Microgrid droop control refers to a double closed-loop control system, which is composed of multiple PI controllers in series and parallel [1]. e control effect of the system varies in cases involving different PI parameters
Because the microgrid is a dynamic system, when the system topology changes, the conventional PI controller cannot adapt to the changes in the system parameters, thereby reducing the response speed of the inverter. e output power, frequency, and voltage of the inverter tend to be out of limit and oscillatory, and the dynamic performance and steady-state accuracy of droop control are reduced. erefore, it is necessary to adjust the PI parameters online in real time
E thermoelectric and wind energy integrated energysaving system was built by Reddy et al [3], the adaptive differential evolution algorithm was used to optimize the solution, and it makes the operating cost of the microgrid and the emission of pollutants be controlled in the best condition. is kind of literature is devoted to the research of microgrid optimization dispatching, by establishing the economic and environmental pollution model of the microgrid, using intelligent algorithm optimization that reduces operating costs and environmental pollution
Summary
Microgrid droop control refers to a double closed-loop control system, which is composed of multiple PI controllers in series and parallel [1]. e control effect of the system varies in cases involving different PI parameters. Many scholars have proposed improvement methods, the adaptive ability of droop control strategies in events including microgrid topology changes has not been considered. Erefore, in the context of the online optimization of droop control PI parameters, the output of the droop control inverter cannot be fed back in time, and the convergence and diversity of the algorithm cannot be adjusted in time, causing the algorithm to perform many invalid calculations. To enhance the adaptive ability of the inverter of a microgrid droop control system, this paper analyzes the basic principle of droop control and limitations of the standard fruit fly algorithm. In terms of droop control optimization, (1) the absolute value integral term of the PI error derivation is introduced in the objective function of microgrid online optimization, and the oscillation and deviation of the inverter output power are effectively suppressed. In terms of droop control optimization, (1) the absolute value integral term of the PI error derivation is introduced in the objective function of microgrid online optimization, and the oscillation and deviation of the inverter output power are effectively suppressed. (2) In noninitial optimization, the use of adaptive and optimal delivery strategies shortens the online optimization time
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