Feature Extraction and Pattern Recognition of Signals Radiated from Partial Discharge

被引：0
|
作者：
Liu Weidong [1 ]
Liu Shanghe [1 ]
Hu Xiaofeng [1 ]
机构：
[1] Mech Engn Coll, Electrostat & Electromagnet Protect Res Inst, Shijiazhuang, Hebei, Peoples R China
来源：
CEEM: 2009 5TH ASIA-PACIFIC CONFERENCE ON ENVIRONMENTAL ELECTROMAGNETICS | 2009年
关键词：
partial discharge; BP network; feature extraction; pattern recognition;
D O I：
暂无
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
The artificial neural networks based BP algorithm is used to recognize two typical discharge patterns, corona and spark. In order to have a comparison, feature extraction based on waveform parameter and time-frequency analysis were used separately to provide the training input. The results show that the highest average recognition rate based on waveform parameter reaches 92.5%, while this based on time-frequency is 95%. On the contrary, the lowest average recognition rate based on waveform parameter is 70%, while this based on time-frequency is 90%. This indicates that time-frequency analysis is more effective and more suitable for discharge pattern recognition.
引用
收藏
页码：114 / 117
页数：4
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