检索结果-南通市图书馆

PV Maximum Power-point Tracking Using Modified Particle Swarm Optimization under Partial Shading Conditions

Chinese Journal of Electrical Engineering 2020年第4期6卷 106-121页

作者： al-wesabi Ibrahim M.B.Shafik Min Ding Mohammad Abu Sarhan Zhijian Fang Ahmed.G.alareqi tariq al’moqri Ayman.M.al-Rassas School of Automation China University of GeoscienceWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China School of Oil and Natural Gas Engineering China University of GeosciencesWuhan 430074China School of Mathematics and Physics China University of GeoscienceWuhan 430074China School of Petroleum Engineering China University of PetroleumEast ChinaQingdao 266580China

A novel maximum power-point tracking approach is proposed based on studies investigating the output characteristics of photovoltaic(PV)systems under partial shading *** existence of partially shaded conditions leads to the presence of several peaks on PV curves,which decrease the efficiency of conventional ***,the proposed algorithm,which is based on the modified particle-swarm optimization(MPSO)technique,increases the output power of PV systems under such abnormal conditions and has a better performance compared to other *** proposed method is examined under several scenarios for partial shading condition and non-uniform irradiation levels using Matlab,and to investigate its effectiveness adequately,the results of the proposed method are compared with those of the neural network *** experimental results show that the proposed method can decrease the interference of the local maximum power-point to cause the PV system to operate at a global maximum *** efficiency of the MPSO is achieved with the least number of steady-state oscillations under partial shading conditions compared with the neural network method.

关键词： Photovoltaic(PV) maximum power point tracking(MPPT) step-up converter artificial neural networks(ANNs) particle-swarm optimization(PSO)

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：