DocumentCode :
1631280
Title :
Intelligent Data Mining Approach for Fault Diagnosis
Author :
Huang, Yann-Chang ; Sun, Huo-Ching ; Lin, Yu-Hsun
Author_Institution :
Dept. of Electr. Eng., Cheng Shiu Univ., Kaohsiung
Volume :
1
fYear :
2008
Firstpage :
303
Lastpage :
306
Abstract :
This paper presents wavelet analysis and statistical techniques for assessing the insulation condition of power cables. A specific fault is made and placed on the terminal joint of a 25 kV power cable, and the deterioration phenomena is accelerated by the overvoltage method. The deterioration phenomena of the internal insulation material are explained by wavelet analysis and statistical techniques using partial discharge (PD) current pulse waveforms. The PD value reaches its maximum level, and average discharge level rises, before insulation breakdown. However, the discharge numbers and the equivalent time-length of partial discharge current pulse waveforms fall, causing a current pulse with a large amplitude, and a short time period in the final stage of PD. The proposed method is demonstrated to be effective and feasible.
Keywords :
data mining; fault diagnosis; power cable insulation; power engineering computing; statistical analysis; wavelet transforms; data mining; fault diagnosis; insulation; partial discharge current pulse waveforms; power cables; statistical techniques; wavelet analysis; Circuit faults; Data mining; Fault diagnosis; Insulation; Partial discharge measurement; Partial discharges; Power cables; Signal processing; Underground power cables; Voltage;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems Design and Applications, 2008. ISDA '08. Eighth International Conference on
Conference_Location :
Kaohsiung
Print_ISBN :
978-0-7695-3382-7
Type :
conf
DOI :
10.1109/ISDA.2008.201
Filename :
4696221
Link To Document :
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