Fuel Cell (FC), as a type of new renewable energy sources grid-connected at Point of Common Coupling (PCC), is introduced in this study. This article presents the power quality improvement of the FC integrated to the power network through a chopper and an inverter using the conventional PI controller. Two PI controllers, tuned by three recent different evolutionary computing techniques namely Harmony Search (HS), Modified Flower Pollination Algorithm (MFPA) and Electromagnetic Field Optimization (EFO) methods are considered. The two PI controllers are used for driving the inverter connected the on-grid FC in order to govern the PCC voltage between the FC and the power network. These two controllers are exploited to drive the power and the current regulators at different voltage sag and swell conditions. The three optimization methods are compared to the Particle Swarm Optimization (PSO) with regards to voltage profile, power quality and execution time.

Simulation results, using Matlab/Simulink^™, show the significance of the three optimization techniques in regulating the voltage at PCC with reduced harmonics during the system voltage sag and swell conditions when compared to the PSO. Through the numerical analysis, the superiority of MFPA method among the different optimization metaheuristic techniques is highlighted particularly for enhanced dynamic voltage response purposes.

这项研究介绍了燃料电池（FC），它是一种在公共耦合点（PCC）并网的新型可再生能源。本文介绍了通过使用常规PI控制器的斩波器和逆变器，集成到电网的FC的电能质量改进。考虑了两个PI控制器，它们通过三种最新的不同进化计算技术（即和声搜索（HS），改进的花授粉算法（MFPA）和电磁场优化（EFO）方法）进行了调整。两个PI控制器用于驱动连接到并网FC的逆变器，以便控制FC和电网之间的PCC电压。利用这两个控制器在不同的电压骤降和骤升条件下驱动电源和电流调节器。

使用Matlab / Simulink ^™进行的仿真结果表明，与PSO相比，这三种优化技术在降低系统电压骤降和骤升条件期间以降低的谐波调节PCC电压方面的重要性。通过数值分析，特别强调了MFPA方法在不同优化元启发式技术中的优越性，以增强动态电压响应的目的。