基于滑模控制和模糊小波网络的光伏水泵系统最大功率点跟踪

Bouchra Sefriti, O. Dahhani, I. Boumhidi
{"title":"基于滑模控制和模糊小波网络的光伏水泵系统最大功率点跟踪","authors":"Bouchra Sefriti, O. Dahhani, I. Boumhidi","doi":"10.1109/ISACV.2015.7106183","DOIUrl":null,"url":null,"abstract":"This paper presents a maximum power point tracking method (MPPT) that combines fuzzy wavelet network with sliding mode control for a photovoltaic pumping system. For the best use, the photovoltaic (PV) generator must operate at its maximum power point (MPP). SMC uses a high switching gain to cover the neglected uncertainties in the system model. However, the SMC produces chattering phenomenon due to the higher needed switching gain, in the presence of large uncertainties. In order to reduce this gain, fuzzy wavelet network (FWN) technique is used in this work to predict the unknown part of the PV pumping system model, which enables the well description of the real system.","PeriodicalId":426557,"journal":{"name":"2015 Intelligent Systems and Computer Vision (ISCV)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Maximum power point tracking for a photovoltaic water pumping system with sliding mode control and fuzzy wavelet network\",\"authors\":\"Bouchra Sefriti, O. Dahhani, I. Boumhidi\",\"doi\":\"10.1109/ISACV.2015.7106183\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a maximum power point tracking method (MPPT) that combines fuzzy wavelet network with sliding mode control for a photovoltaic pumping system. For the best use, the photovoltaic (PV) generator must operate at its maximum power point (MPP). SMC uses a high switching gain to cover the neglected uncertainties in the system model. However, the SMC produces chattering phenomenon due to the higher needed switching gain, in the presence of large uncertainties. In order to reduce this gain, fuzzy wavelet network (FWN) technique is used in this work to predict the unknown part of the PV pumping system model, which enables the well description of the real system.\",\"PeriodicalId\":426557,\"journal\":{\"name\":\"2015 Intelligent Systems and Computer Vision (ISCV)\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 Intelligent Systems and Computer Vision (ISCV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISACV.2015.7106183\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Intelligent Systems and Computer Vision (ISCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISACV.2015.7106183","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

提出了一种将模糊小波网络与滑模控制相结合的光伏泵系统最大功率点跟踪方法。为了达到最佳使用效果,光伏(PV)发电机必须在其最大功率点(MPP)运行。SMC使用高开关增益来覆盖系统模型中被忽略的不确定性。然而,由于需要较高的开关增益,在存在较大的不确定性的情况下,SMC会产生抖振现象。为了减小这一增益,本文采用模糊小波网络(FWN)技术对光伏抽水系统模型中的未知部分进行预测,使其能够较好地描述实际系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Maximum power point tracking for a photovoltaic water pumping system with sliding mode control and fuzzy wavelet network
This paper presents a maximum power point tracking method (MPPT) that combines fuzzy wavelet network with sliding mode control for a photovoltaic pumping system. For the best use, the photovoltaic (PV) generator must operate at its maximum power point (MPP). SMC uses a high switching gain to cover the neglected uncertainties in the system model. However, the SMC produces chattering phenomenon due to the higher needed switching gain, in the presence of large uncertainties. In order to reduce this gain, fuzzy wavelet network (FWN) technique is used in this work to predict the unknown part of the PV pumping system model, which enables the well description of the real system.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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:604180095
Book学术官方微信