• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于元启发式优化的混合模糊逻辑-PI控制，用于提高高渗透率并网光伏系统的性能。

Hybrid fuzzy logic-PI control with metaheuristic optimization for enhanced performance of high-penetration grid-connected PV systems.

作者信息

Mohamed Mohamed Ahmed Ebrahim, Ward Sayed A, El-Gohary Mohamed F, Mohamed M A

机构信息

Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo, Egypt.

Faculty of Engineering, Delta University for Science and Technology, Gamasa, 35712, Egypt.

出版信息

Sci Rep. 2025 Jul 9;15(1):24650. doi: 10.1038/s41598-025-09336-w.

DOI:10.1038/s41598-025-09336-w
PMID:40634425
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12241569/
Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/0a76cba6d1b8/41598_2025_9336_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/c91b211663fc/41598_2025_9336_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/b0258c87bc31/41598_2025_9336_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/50c8bf2a9e4c/41598_2025_9336_Figb_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/9a859c2b37ac/41598_2025_9336_Figc_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/eaa693948247/41598_2025_9336_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/40298e049ff4/41598_2025_9336_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/79ccf01447ff/41598_2025_9336_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/6f5b66a7885a/41598_2025_9336_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/5365141855d0/41598_2025_9336_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/4e725204674d/41598_2025_9336_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/13c4174bb024/41598_2025_9336_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/5133f38737a7/41598_2025_9336_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/8148cfd4adb0/41598_2025_9336_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/71ec35e41a9a/41598_2025_9336_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/827691ef0d5c/41598_2025_9336_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/cf050449aa3b/41598_2025_9336_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/c5da3a95b813/41598_2025_9336_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/7c9084ab8d30/41598_2025_9336_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/0a76cba6d1b8/41598_2025_9336_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/c91b211663fc/41598_2025_9336_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/b0258c87bc31/41598_2025_9336_Figa_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/50c8bf2a9e4c/41598_2025_9336_Figb_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/9a859c2b37ac/41598_2025_9336_Figc_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/eaa693948247/41598_2025_9336_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/40298e049ff4/41598_2025_9336_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/79ccf01447ff/41598_2025_9336_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/6f5b66a7885a/41598_2025_9336_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/5365141855d0/41598_2025_9336_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/4e725204674d/41598_2025_9336_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/13c4174bb024/41598_2025_9336_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/5133f38737a7/41598_2025_9336_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/8148cfd4adb0/41598_2025_9336_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/71ec35e41a9a/41598_2025_9336_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/827691ef0d5c/41598_2025_9336_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/cf050449aa3b/41598_2025_9336_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/c5da3a95b813/41598_2025_9336_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/7c9084ab8d30/41598_2025_9336_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3178/12241569/0a76cba6d1b8/41598_2025_9336_Fig16_HTML.jpg

相似文献

1
Hybrid fuzzy logic-PI control with metaheuristic optimization for enhanced performance of high-penetration grid-connected PV systems.基于元启发式优化的混合模糊逻辑-PI控制，用于提高高渗透率并网光伏系统的性能。
Sci Rep. 2025 Jul 9;15(1):24650. doi: 10.1038/s41598-025-09336-w.
2
Enhancing MPPT optimization with hybrid predictive control and adaptive P&O for better efficiency and power quality in PV systems.采用混合预测控制和自适应扰动观察法增强最大功率点跟踪（MPPT）优化，以提高光伏系统的效率和电能质量。
Sci Rep. 2025 Jul 8;15(1):24559. doi: 10.1038/s41598-025-10335-0.
3
Comparative analysis of reinforcement learning and artificial neural networks for inverter control in improving the performance of grid-connected photovoltaic systems.用于改善并网光伏系统性能的逆变器控制中强化学习与人工神经网络的对比分析
Sci Rep. 2025 Jul 8;15(1):24477. doi: 10.1038/s41598-025-09507-9.
4
Optimized frequency stabilization in hybrid renewable power grids with integrated energy storage systems using a modified fuzzy-TID controller.使用改进型模糊-TID控制器的集成储能系统在混合可再生能源电网中的优化频率稳定
Sci Rep. 2025 Jun 20;15(1):20095. doi: 10.1038/s41598-025-02011-0.
5
Dual-tree wavelet transform based advanced adaptive control for seamless transition in PV-battery hybrid microgrid system.基于双树小波变换的先进自适应控制在光伏-电池混合微电网系统中的无缝切换
Sci Rep. 2025 Jul 1;15(1):20464. doi: 10.1038/s41598-025-05398-y.
6
Introducing electric spring in the voltage frequency regulation of a multi area multi source integrated power system network.在多区域多源集成电力系统网络的电压频率调节中引入电力弹簧。
Sci Rep. 2025 Jul 1;15(1):22373. doi: 10.1038/s41598-025-05576-y.
7
Integrated MPPT and bidirectional DC DC converter with reduced switch multilevel inverters for electric vehicles applications.用于电动汽车应用的集成最大功率点跟踪和双向直流-直流转换器以及精简开关多电平逆变器。
Sci Rep. 2025 Jul 11;15(1):25053. doi: 10.1038/s41598-025-08700-0.
8
Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions.在可变大气条件下采用新型混合最大功率点跟踪技术提高并网光伏系统性能
Sci Rep. 2024 Apr 8;14(1):8205. doi: 10.1038/s41598-024-59024-4.
9
A hybrid renewable energy system with advanced control strategies for improved grid stability and power quality.一种具有先进控制策略的混合可再生能源系统，用于提高电网稳定性和电能质量。
Sci Rep. 2025 Jul 2;15(1):23445. doi: 10.1038/s41598-025-06091-w.
10
Particle swarm optimization algorithm-based PI inverter controller for a grid-connected PV system.基于粒子群优化算法的光伏系统并网 PI 逆变器控制器。
PLoS One. 2020 Dec 23;15(12):e0243581. doi: 10.1371/journal.pone.0243581. eCollection 2020.

本文引用的文献

1
Electric Eel foraging optimization based control design of islanded microgrid.基于电鳗觅食优化的孤岛微电网控制设计
Sci Rep. 2025 Mar 9;15(1):8144. doi: 10.1038/s41598-025-91006-y.
2
Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids.基于混合猎豹粒子群优化算法的多微电网最优分层控制
Sci Rep. 2024 Apr 23;14(1):9313. doi: 10.1038/s41598-024-59287-x.
3
Modern PID/FOPID controllers for frequency regulation of interconnected power system by considering different cost functions.
通过考虑不同成本函数用于互联电力系统频率调节的现代比例积分微分/分数阶比例积分微分控制器。
Sci Rep. 2023 Aug 28;13(1):14084. doi: 10.1038/s41598-023-41024-5.
4
Power quality improvement of a proposed grid-connected hybrid system by load flow analysis using static var compensator.基于使用静止无功补偿器的潮流分析对拟议的并网混合系统进行电能质量改善
Heliyon. 2023 Jul 3;9(7):e17915. doi: 10.1016/j.heliyon.2023.e17915. eCollection 2023 Jul.
5
Arithmetic optimization algorithm based maximum power point tracking for grid-connected photovoltaic system.基于算术优化算法的光伏系统最大功率点跟踪并网。
Sci Rep. 2023 Apr 12;13(1):5961. doi: 10.1038/s41598-023-32793-0.