NDLI: Feature extraction and pattern recognition of signals radiated from partial discharge

Content Provider	IEEE Xplore Digital Library
Author	Liu Weidong Liu Shanghe Hu Xiaofeng
Copyright Year	2009
Description	Author affiliation: Electrostatic and Electromagnetic Protection Research Institute, Mechanical Engineering College, Post Code: 050003, Shijiazhuang, Hebei, China (Liu Weidong; Liu Shanghe; Hu Xiaofeng)
Abstract	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.
Starting Page	114
Ending Page	117
File Size	690079
Page Count	4
File Format	PDF
ISBN	9781424443444
DOI	10.1109/CEEM.2009.5304189
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Pattern recognition Partial discharges Artificial neural networks Corona Sparks Time frequency analysis Partial discharge measurement Electromagnetic radiation Voltage pattern recognition partial discharge BP network feature extraction
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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