A Sugeno-Fuzzy Tuning Approach of Weighting Factor in Model Predictive Control for PV Grid-Tied PUC7 Multi-Level Inverter

B. Talbi, F. Krim, Abdelbaset Laib, Abdeslem Sahli, B. Babes
{"title":"A Sugeno-Fuzzy Tuning Approach of Weighting Factor in Model Predictive Control for PV Grid-Tied PUC7 Multi-Level Inverter","authors":"B. Talbi, F. Krim, Abdelbaset Laib, Abdeslem Sahli, B. Babes","doi":"10.1109/SGRE53517.2022.9774159","DOIUrl":null,"url":null,"abstract":"Recently, the application of model predictive control (MPC) in energy conversion systems has been extensively investigated both theoretically and experimentally. Different MPC techniques have been proposed to control multi-level inverters in grid-tied operation, permitting high performance and fast dynamic response. The optimization employed in MPC strategies is based on a cost function minimization, where control purposes are combined by using weighting factors. Nevertheless, the choice of weighting factors is attained through offline and online search approaches and they are heavily dependent on the system parameters. To overcome this drawback, an online tuning of weighting factor based on Sugeno-fuzzy approach in MPC for photovoltaic (PV) grid-tied system using PUC7 (Seven-level packed U-cell) inverter is suggested in this work. Simulation tests are presented to confirm the performance of the proposed control strategy.","PeriodicalId":64562,"journal":{"name":"智能电网与可再生能源(英文)","volume":"36 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"智能电网与可再生能源(英文)","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.1109/SGRE53517.2022.9774159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Recently, the application of model predictive control (MPC) in energy conversion systems has been extensively investigated both theoretically and experimentally. Different MPC techniques have been proposed to control multi-level inverters in grid-tied operation, permitting high performance and fast dynamic response. The optimization employed in MPC strategies is based on a cost function minimization, where control purposes are combined by using weighting factors. Nevertheless, the choice of weighting factors is attained through offline and online search approaches and they are heavily dependent on the system parameters. To overcome this drawback, an online tuning of weighting factor based on Sugeno-fuzzy approach in MPC for photovoltaic (PV) grid-tied system using PUC7 (Seven-level packed U-cell) inverter is suggested in this work. Simulation tests are presented to confirm the performance of the proposed control strategy.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
PV并网PUC7多级逆变器模型预测控制中权重因子的Sugeno-Fuzzy整定方法
近年来,模型预测控制(MPC)在能量转换系统中的应用得到了广泛的理论和实验研究。不同的MPC技术被提出用于控制并网运行的多级逆变器,以实现高性能和快速动态响应。MPC策略中采用的优化是基于成本函数最小化的,其中控制目的通过使用加权因子进行组合。然而,权重因子的选择是通过离线和在线搜索方法获得的,它们严重依赖于系统参数。为了克服这一缺点,本文提出了一种基于Sugeno-fuzzy方法的PV并网系统MPC加权因子在线整定方法。仿真实验验证了所提控制策略的有效性。
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 去求助
来源期刊
自引率
0.00%
发文量
307
期刊最新文献
Experimental Investigations of the Effects of Secondary Air Injection on Gaseous Emission Profiles (NOx, NO, NO2, CO) and Hydrocarbons (CxHx) in Cookstoves Using Charcoal from Eucalyptus glandis Microgrid Optimal Scheduling Carbon and Water Footprint Evaluation of 120Wp Rural Household Photovoltaic System: Case Study Performance of the Boost Chopper, Comparative Study between PI Control and Neural Control to Regulate Its Output Voltage An Energy Production System Powered by Solar Heat with Biogas Dry Reforming Reactor and Solid Oxide Fuel Cell
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1