检索结果-内蒙古大学图书馆

Efficient maximum power point tracking based on reweighted zero-attracting variable stepsize for grid interfaced photovoltaic systems

引用

COMPUTERS & ELECTRICAL ENGINEERING 2020年 85卷 106672-106672页

作者： Mohamed-Kazim, Haider A. Abdel-Qader, Ikhlas Harb, Ahmad M. Western Michigan Univ Elect & Comp Engn Dept Kalamazoo MI 49008 USA German Jordanian Univ Energy Engn Dept Amman Jordan

Maximum Power Point Tracking (MPPT) algorithms mainly govern the performances of solar photovoltaic (PV) array of smart grid systems. To optimize systems' output power in both steady-state and transient situations of the tracking process, a framework using an individualized sparse-aware time-adjusting stepsize adaptation technique for the traditional MPPT method is presented. The objective of this framework is to adaptively predict the produced power of a PV system instead of utilizing the currently estimated power to achieve better performance with the improved tracking abilities. This framework is based on predicting the power using the previously estimated values of the PV output voltage. In addition, the adaptation process is based on a time-varying stepsize that adjusts in accordance with the value of PV voltage. This approach presents a superior performance compared to the conventional method by providing an outstanding tracking ability and maintaining a stable performance under rapid alterations of atmospheric circumstances. (c) 2020 Elsevier Ltd. All rights reserved.

关键词： Solar photovoltaic array Adaptive Linear Prediction Filter Sparse-Aware algorithms time-varying stepsize algorithms MPPT algorithms Perturb & Observe Method

来源：评论

学校读者我要写书评

暂无评论

A Fast and Robust System Identification on Compressive Sensing Signal Recovery Based on Multiple time-Vary Step-size Adaptation Technique

A Fast and Robust System Identification on Compressive Sensi...

引用

IEEE National Aerospace and Electronics Conference (NAECON)

作者： Kazim, Haider A. Mohamed Abdel-Qader, Ikhlas Western Michigan Univ Elect & Comp Engn Dept Kalamazoo MI 49008 USA

ISBN: (纸本)9781728114163

To improve the robustness of the algorithm against unknown sparsity levels, and to reduce the trading-off between complexity and quality of recovering sparse signals, a new technique for sparse signal reconstruction in compressive sensing (CS) is presented. The solution presented in this work based on a recently proposed algorithm that innovatively employs a tap-individualized time-varying stepsize for system identification. It converts the matrix operation of existing approaches to vector implementation and exploits the well-known property of time-varying stepsize algorithms of noise-tolerance in signal reconstruction. The essence is to allocate each tap a unique time-varying stepsize that updates according to the power of each row of the measurement matrix to control the step of decreasing of each stepsize individually. Advantages of the proposed technique such as robustness against sparsity level and noisy signals, and achieving faster convergence rate are demonstrated numerically.

关键词： Adaptive Sparse system identification Compressed sensing time-varying stepsize algorithms Least Mean Square (LMS) l(0) - norm

来源：评论

学校读者我要写书评

暂无评论

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

请选择保存的检索档案：

请选择收藏分类：

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

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

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：