NDLI: PD pattern recognition of power capacitors model based on Combinational neural network

Content Provider	IEEE Xplore Digital Library
Author	Shengyou Gao Fuqi Li Cangbi Yu Kexiong Tan
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. Eng., Tsinghua Univ., Beijing, China (Shengyou Gao; Fuqi Li; Cangbi Yu; Kexiong Tan)
Abstract	Five types of partial discharge (PD) models are designed to represent typical PD phenomena in power capacitors. A computer-based acoustic emission signal detecting instrument that is developed for PD is used to collect a lot of acoustic signals of model discharge. The acoustic signal duration of gas cavity discharge is very short, therefore, it is easy to be differentiated from other discharge patterns. According to the time domain and frequency domain graph of acoustic signals, the characters of oil gap discharge is close to that of discharge along oil impregnated paper surface. However, the character of discharge of metallic impurity in oil impregnated paper insulation is close to that of surface discharge of bushing. Combinational neural network (CNN) is used to recognize the five kinds of representative discharge patterns. The result shows that CNN is effective and the characteristics of acoustic signals could be used to recognize PD patterns in power capacitors.
Sponsorship	IEEE Dielectrics & Electrical Insulation Soc. IEE Japan
Starting Page	319
Ending Page	322
File Size	182544
Page Count	4
File Format	PDF
ISBN	0780377257
ISSN	10817735
DOI	10.1109/ICPADM.2003.1218416
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-06-01
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern recognition Power capacitors Neural networks Partial discharges Acoustic emission Petroleum Surface discharges Cellular neural networks Signal detection Acoustic signal detection
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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