Optimal PV Generation Using Symbiotic Organisms Search Optimization Algorithm-Based MPPT

Alper Nabi Akpolat, Y. A. Baysal, Yongheng Yang, F. Blaabjerg
{"title":"Optimal PV Generation Using Symbiotic Organisms Search Optimization Algorithm-Based MPPT","authors":"Alper Nabi Akpolat, Y. A. Baysal, Yongheng Yang, F. Blaabjerg","doi":"10.1109/IECON43393.2020.9254849","DOIUrl":null,"url":null,"abstract":"In this period when the technology has been developing rapidly, resources are being exhausted as well conversely. Therefore, possible problems and the ways of handling them are changing and new problem-solving techniques are being tried. Due to the intermittent nature of photovoltaic (PV) systems, which have solar irradiance and temperature as a source, the problem of maximum power attaining arises. The solution to this problem aims to make optimal use of PV energy production. This study presents a metaheuristic algorithm to solve the problem of maximum power point tracking (MPPT) from PV systems which are an indispensable part of renewable energy technology. Symbiotic organisms search (SOS), a powerful and dynamic metaheuristic optimization algorithm, is adopted as a solution to this problem. The SOS algorithm has been inspired by the symbiotic interactions adopted their behavior to survive in the ecosystem, which has developed to solve optimization and engineering problems. The proposed algorithm, i.e., SOS, has been embedded in MATLAB/Simulink platform to test for accuracy and efficiency. From the obtained results, this evolutionary SOS algorithm is seen obviously to outperform in certain points more than the classical Perturb and Observe (P&O) and Incremental Conductance (INC) methods for the same system and conditions.","PeriodicalId":13045,"journal":{"name":"IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society","volume":"63 1","pages":"2850-2855"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IECON43393.2020.9254849","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

In this period when the technology has been developing rapidly, resources are being exhausted as well conversely. Therefore, possible problems and the ways of handling them are changing and new problem-solving techniques are being tried. Due to the intermittent nature of photovoltaic (PV) systems, which have solar irradiance and temperature as a source, the problem of maximum power attaining arises. The solution to this problem aims to make optimal use of PV energy production. This study presents a metaheuristic algorithm to solve the problem of maximum power point tracking (MPPT) from PV systems which are an indispensable part of renewable energy technology. Symbiotic organisms search (SOS), a powerful and dynamic metaheuristic optimization algorithm, is adopted as a solution to this problem. The SOS algorithm has been inspired by the symbiotic interactions adopted their behavior to survive in the ecosystem, which has developed to solve optimization and engineering problems. The proposed algorithm, i.e., SOS, has been embedded in MATLAB/Simulink platform to test for accuracy and efficiency. From the obtained results, this evolutionary SOS algorithm is seen obviously to outperform in certain points more than the classical Perturb and Observe (P&O) and Incremental Conductance (INC) methods for the same system and conditions.
基于共生生物搜索优化算法的最优光伏发电
在这个技术飞速发展的时期,资源也在逐渐枯竭。因此,可能出现的问题和处理问题的方法正在发生变化,新的解决问题的技术正在尝试。由于以太阳辐照度和温度为来源的光伏(PV)系统的间歇性,产生了获得最大功率的问题。该问题的解决方案旨在优化利用光伏发电。针对可再生能源技术中不可缺少的光伏系统最大功率点跟踪问题,提出了一种元启发式算法。采用一种功能强大的动态元启发式优化算法——共生生物搜索(SOS)来解决这一问题。SOS算法受到共生相互作用的启发,采用它们的行为在生态系统中生存,并发展为解决优化和工程问题。将所提出的SOS算法嵌入到MATLAB/Simulink平台中,对其精度和效率进行了测试。从得到的结果来看,对于相同的系统和条件,该进化SOS算法在某些点上明显优于经典的扰动和观察(P&O)和增量电导(INC)方法。
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
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信