.png){kind=icon}
返回
Power quality event detection using joint 2-D-wavelet subspaces
.png){kind=icon}
PRE
AI
DOI：10.1109/TIM.2004.831137.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this work, we present a novel two-dimensional (2-D) representation of power system waveforms for the automatic analysis and detection of transient events. The representation is composed of a matrix whose rows are formed by time segments of digital waveforms. By the appropriate selection of the time segment length, the 2-D data exhibits wave-like image shapes. The general shape is immediately disturbed whenever a power quality transient event occurs. We propose the use of two dimensional discrete wavelet transforms (2-D-DWT) to detect these disturbances. It has been observed that, after omitting the approximation space signals of the wavelet transform and denoising the detail space signals, the inverse 2-D-DWT provides good detection and localization results, even for cases where conventional methods fail. Examples are presented.
Keyword:
event detection
power quality
two-dimensional (2-D) wavelet decomposition
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
暂无机构信息
引用论文
Power quality detection and classification using wavelet-multiresolution signal decomposition基于小波多分辨率信号分解的电能质量检测与分类
PREAI
分享收藏Preventive pharmacological treatment in subjects at risk for fatal familial insomnia: science and public engagement.png)
Prion
OAAI
PrionIF0
OAAI分享收藏On the Thermodynamic Equivalence of Grand Canonical, Infinite‐Size, and Capacitor‐Based Models in First‐Principle ElectrochemistryChemPhysChemOAAI
ChemPhysChemIF0
OAAI分享收藏 没有更多内容
没有更多内容