Electric eel foraging algorithm-based optimal control for low voltage ride through capability improvement of Grid-Connected photovoltaic power plants

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal Pub Date : 2024-07-01 DOI:10.1016/j.asej.2024.102855

Nourhan A. Maged , Hany M. Hasanien , Mohammed Alharbi

{"title":"Electric eel foraging algorithm-based optimal control for low voltage ride through capability improvement of Grid-Connected photovoltaic power plants","authors":"Nourhan A. Maged , Hany M. Hasanien , Mohammed Alharbi","doi":"10.1016/j.asej.2024.102855","DOIUrl":null,"url":null,"abstract":"<div><p>This paper presents a novel application of the Electric Eel Foraging Optimization (EEFO) algorithm. An optimal control technique based on EEFO is used to improve grid-connected photovoltaic (PV) power plants’ low-voltage ride-through (LVRT) capabilities. The PV arrays are connected to the grid at the point of common coupling (PCC) through a DC-DC boost converter, DC-link capacitor, a grid-side inverter and a three-phase step-up transformer. The DC-DC converter circuit is implemented to obtain the maximum power point tracking operation based on the incremental conductance method. The grid-side inverter uses the cascaded control loop to regulate the DC-link voltage and the terminal voltage at the PCC. Because of its highly rapid convergence, the optimized proportional-integral (PI) controller based on the EEFO algorithm is utilized to regulate the power of electronic circuits. This paper also presents a comparative analysis among the results using the EEFO, genetic, and Particle swarm optimization algorithms. This comparison verifies the effectiveness of the control strategy based on the EEFO algorithm than other techniques. It is studied also by subject the system to symmetrical and unsymmetrical faults as well as unsuccessful circuit breaker reclosing caused by the presence of a permanent fault. MATLAB/Simulink software package is used to provide substantial validations of the proposed control technique. The LVRT capability of such a system can be improved using the proposed methodology.</p></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2090447924002302/pdfft?md5=35bb6737bdb49a7972072e4d0efea3c1&pid=1-s2.0-S2090447924002302-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ain Shams Engineering Journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2090447924002302","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

This paper presents a novel application of the Electric Eel Foraging Optimization (EEFO) algorithm. An optimal control technique based on EEFO is used to improve grid-connected photovoltaic (PV) power plants’ low-voltage ride-through (LVRT) capabilities. The PV arrays are connected to the grid at the point of common coupling (PCC) through a DC-DC boost converter, DC-link capacitor, a grid-side inverter and a three-phase step-up transformer. The DC-DC converter circuit is implemented to obtain the maximum power point tracking operation based on the incremental conductance method. The grid-side inverter uses the cascaded control loop to regulate the DC-link voltage and the terminal voltage at the PCC. Because of its highly rapid convergence, the optimized proportional-integral (PI) controller based on the EEFO algorithm is utilized to regulate the power of electronic circuits. This paper also presents a comparative analysis among the results using the EEFO, genetic, and Particle swarm optimization algorithms. This comparison verifies the effectiveness of the control strategy based on the EEFO algorithm than other techniques. It is studied also by subject the system to symmetrical and unsymmetrical faults as well as unsuccessful circuit breaker reclosing caused by the presence of a permanent fault. MATLAB/Simulink software package is used to provide substantial validations of the proposed control technique. The LVRT capability of such a system can be improved using the proposed methodology.

查看原文本刊更多论文

基于电鳗觅食算法的优化控制，提高并网光伏电站的低电压穿越能力

本文介绍了电鳗觅食优化（EEFO）算法的一种新型应用。基于 EEFO 的优化控制技术用于提高并网光伏（PV）电站的低电压穿越（LVRT）能力。光伏阵列通过直流-直流升压转换器、直流链路电容器、电网侧逆变器和三相升压变压器在共耦点（PCC）与电网相连。直流-直流转换器电路基于增量电导法实现最大功率点跟踪运行。电网侧逆变器使用级联控制环路调节直流链路电压和 PCC 端电压。基于 EEFO 算法的优化比例积分 (PI) 控制器收敛速度极快，因此可用于调节电子电路的功率。本文还对 EEFO 算法、遗传算法和粒子群优化算法的结果进行了比较分析。这种比较验证了基于 EEFO 算法的控制策略比其他技术更有效。此外，还研究了系统在对称和非对称故障以及永久故障导致断路器重合不成功的情况下的控制策略。MATLAB/Simulink 软件包用于对所提出的控制技术进行实质性验证。利用所提出的方法，可以提高此类系统的低电压穿越能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Ain Shams Engineering Journal Engineering-General Engineering

CiteScore

10.80

自引率

13.30%

发文量

441

审稿时长

49 weeks

期刊介绍： in Shams Engineering Journal is an international journal devoted to publication of peer reviewed original high-quality research papers and review papers in both traditional topics and those of emerging science and technology. Areas of both theoretical and fundamental interest as well as those concerning industrial applications, emerging instrumental techniques and those which have some practical application to an aspect of human endeavor, such as the preservation of the environment, health, waste disposal are welcome. The overall focus is on original and rigorous scientific research results which have generic significance. Ain Shams Engineering Journal focuses upon aspects of mechanical engineering, electrical engineering, civil engineering, chemical engineering, petroleum engineering, environmental engineering, architectural and urban planning engineering. Papers in which knowledge from other disciplines is integrated with engineering are especially welcome like nanotechnology, material sciences, and computational methods as well as applied basic sciences: engineering mathematics, physics and chemistry.