Model Predictive Control Based Grid Connected Inverter for Renewable Energy Applications

K. Balakrishnan, K. Yasoda
{"title":"Model Predictive Control Based Grid Connected Inverter for Renewable Energy Applications","authors":"K. Balakrishnan, K. Yasoda","doi":"10.36548/jei.2022.1.003","DOIUrl":null,"url":null,"abstract":"Model Predictive Control (MPC) for grid-connected inverters has been presented in this paper. The standard proportional-integral controller based system is replaced by this control method for a two-level inverter using Euler's approximation technique to improve the inverter's dynamic response. To anticipate the grid-connected inverter's longer-term behaviour, a replacement predictive mathematical model is offered, which is likened to the reference signal to decide the system's cost function. With this MPC approach, the cost functions of the converter are derived using all possible switching vectors. The associated switching vector for the minimal possible function is then chosen to activate the inverter switches throughout the subsequent sampling instant. The suggested scheme is validated in Simulink to verify its effectiveness and performance. In comparison to the PI-based controller, total harmonic distortion (THD) and current error are minimized.","PeriodicalId":10940,"journal":{"name":"Day 2 Tue, March 22, 2022","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 2 Tue, March 22, 2022","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36548/jei.2022.1.003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Model Predictive Control (MPC) for grid-connected inverters has been presented in this paper. The standard proportional-integral controller based system is replaced by this control method for a two-level inverter using Euler's approximation technique to improve the inverter's dynamic response. To anticipate the grid-connected inverter's longer-term behaviour, a replacement predictive mathematical model is offered, which is likened to the reference signal to decide the system's cost function. With this MPC approach, the cost functions of the converter are derived using all possible switching vectors. The associated switching vector for the minimal possible function is then chosen to activate the inverter switches throughout the subsequent sampling instant. The suggested scheme is validated in Simulink to verify its effectiveness and performance. In comparison to the PI-based controller, total harmonic distortion (THD) and current error are minimized.
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
基于模型预测控制的可再生能源并网逆变器
本文提出了并网逆变器的模型预测控制(MPC)。采用欧拉近似技术对两电平逆变器进行控制,取代了基于标准比例积分控制器的系统,提高了逆变器的动态响应。为了预测并网逆变器的长期行为,提出了一个替代预测数学模型,将其比作参考信号来确定系统的成本函数。利用这种MPC方法,利用所有可能的开关向量推导出变换器的代价函数。然后选择最小可能函数的相关开关矢量,以在随后的采样瞬间激活逆变器开关。在Simulink中验证了该方案的有效性和性能。与基于pi的控制器相比,总谐波失真(THD)和电流误差最小。
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
0
期刊最新文献
Selection of Cluster Head in Wireless Sensor Network using Convolution Neural Network Algorithm A Hybrid Wireless Sensor Network Protocol for Time-Sensitive Emergency Operations Internet of Things Driven Smart Cities in Post Pandemic Era Analysis of IoT Enabled Architecture in Various Sectors and their Challenges Detection of Retinal Neovascularization Using Optimized Deep Convolutional Neural Networks
×
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