Mohamed Mohamed Ahmed Ebrahim, Ward Sayed A, El-Gohary Mohamed F, Mohamed M A
Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt.
Faculty of Engineering, Delta University for Science and Technology, Gamasa, 35712, Egypt.
Sci Rep. 2025 Jul 9;15(1):24650. doi: 10.1038/s41598-025-09336-w.
This paper introduces a hybrid fuzzy logic control-based proportional-integral (FLC-PI) control strategy designed to enhance voltage stability, power quality, and overall performance of central inverters in photovoltaic power plants (PVPPs). The study is based on a real-world PVPP with an installed capacity of 26.136 MWp, connected to the Egyptian national grid at Fares City, Kom Ombo Centre, Aswan Governorate. A user-friendly MATLAB/SIMULINK environment is developed, incorporating eleven distinct blocks along with a modelled national utility grid, utilizing actual operational data from the PVPP. To optimize the FLC-PI control scheme, several artificial intelligence (AI)-based metaheuristic optimization techniques (MOTs) are employed to simultaneously tune all control parameters-namely Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and the Arithmetic Optimization Algorithm (AOA)-are employed. These techniques are used to simultaneously fine-tune all the gain parameters of FLC-PI control, based on four standard error-based objective functions: Integral Absolute Error (IAE), Integral Square Error (ISE), Integral Time Absolute Error (ITAE), and Integral Time Square Error (ITSE). The optimized gains are applied to both voltage and current regulators of the central inverters, enabling the identification of optimal values. Among the tested methods, the HHO algorithm combined with the ISE objective function delivered the best performance, achieving a total harmonic distortion (THD) of 3.88%-well below the IEEE 519-2014 limit of 5.00%. The results confirm that the proposed FLC-PI controller significantly enhances the integration of high-penetration PVPPs into the utility grid by reducing power losses and inverter-induced harmonics, especially during maximum power point tracking (MPPT). Moreover, employing MOTs for controller tuning proves to be an effective solution for adapting to dynamic solar irradiance conditions. Ultimately, the optimized FLC-PI control approach enhances voltage stability, improves power quality, and boosts the overall efficiency of grid-connected PV systems.
本文介绍了一种基于混合模糊逻辑控制的比例积分(FLC-PI)控制策略,旨在提高光伏电站(PVPPs)中中央逆变器的电压稳定性、电能质量和整体性能。该研究基于一个实际的PVPP,其装机容量为26.136MWp,连接到埃及阿斯旺省科姆翁布中心法雷斯市的国家电网。开发了一个用户友好的MATLAB/SIMULINK环境,其中包含11个不同的模块以及一个建模的国家公用电网,并利用了PVPP的实际运行数据。为了优化FLC-PI控制方案,采用了几种基于人工智能(AI)的元启发式优化技术(MOTs)来同时调整所有控制参数,即采用了灰狼优化(GWO)、哈里斯鹰优化(HHO)和算术优化算法(AOA)。这些技术用于基于四个基于标准误差的目标函数同时微调FLC-PI控制的所有增益参数:积分绝对误差(IAE)、积分平方误差(ISE)、积分时间绝对误差(ITAE)和积分时间平方误差(ITSE)。将优化后的增益应用于中央逆变器的电压和电流调节器,从而能够确定最佳值。在测试方法中,结合ISE目标函数的HHO算法表现最佳,总谐波失真(THD)为3.88%,远低于IEEE 519-2014规定的5.00%的限值。结果证实,所提出的FLC-PI控制器通过降低功率损耗和逆变器引起的谐波,显著增强了高渗透率PVPPs与公用电网的集成,尤其是在最大功率点跟踪(MPPT)期间。此外,采用MOTs进行控制器调整被证明是适应动态太阳辐照条件的有效解决方案。最终,优化后的FLC-PI控制方法提高了电压稳定性,改善了电能质量,并提高了并网光伏系统的整体效率。