DocumentCode :
2116356
Title :
Partial Discharge Pattern Recognition Using Radial Basis Function Neural Network
Author :
Chang, Wen-Yeau
Author_Institution :
Dept. of Electr. Eng., St. John´´s Univ., Taipei, Taiwan
fYear :
2010
fDate :
28-31 March 2010
Firstpage :
1
Lastpage :
4
Abstract :
This paper proposed a novel radial basis function (RBF) neural network based recognition method to identify the insulation defects of high voltage electrical apparatus arising from partial discharge (PD). Basically, a defect of insulation, as resulting from PD, would have a corresponding particular pattern. Therefore, pattern recognition of PD is significant to discriminate insulation conditions of electrical apparatus. Pattern recognition of PD aims at recognizing the defects causing the PD, such as internal discharge, external discharge, or corona, etc. This information is vital for estimating the harmfulness of the discharge in the insulation. To verify the proposed approach, experiments were conducted to demonstrate the field-test PD pattern recognition of insulators by using feature vectors of field-test PD patterns. The experimental data are found to be in close agreement. The test results show that the proposed approach is efficient and reliable.
Keywords :
electric machine analysis computing; insulation; partial discharges; pattern recognition; power supplies to apparatus; radial basis function networks; corona; external discharge; feature vectors; high voltage electrical apparatus; insulation defects; internal discharge; partial discharge; pattern recognition; radial basis function neural network; Circuit testing; Dielectrics and electrical insulation; Feature extraction; Insulator testing; Mathematical model; Neural networks; Partial discharges; Pattern recognition; Power transformer insulation; Radial basis function networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-4812-8
Electronic_ISBN :
978-1-4244-4813-5
Type :
conf
DOI :
10.1109/APPEEC.2010.5449354
Filename :
5449354
Link To Document :
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