Please use this identifier to cite or link to this item: https://doi.org/10.1109/TDEI.2006.1667751
Title: Online source recognition of partial discharge for gas insulated substations using independent component analysis
Authors: Chang, C.S. 
Jin, J.
Chang, C. 
Hoshino, T.
Hanai, M.
Kobayashi, N.
Keywords: Condition monitoring and insula-tion diagnosis
Gas insulated substations
Independent component analysis
Online source recognition of par-tial discharge and corona
Issue Date: Aug-2006
Source: Chang, C.S., Jin, J., Chang, C., Hoshino, T., Hanai, M., Kobayashi, N. (2006-08). Online source recognition of partial discharge for gas insulated substations using independent component analysis. IEEE Transactions on Dielectrics and Electrical Insulation 13 (4) : 892-902. ScholarBank@NUS Repository. https://doi.org/10.1109/TDEI.2006.1667751
Abstract: To perform reliable insulation diagnosis for gas-insulated substation (GIS), detectable partial discharge (PD) should be identified quickly and effectively. With the increasing application of high voltage dc transmission, PD identification in such systems becomes more and more important. Therefore, a novel technique based on the analysis of ultra high frequency (UHF) resonance waveforms is proposed in this paper to meet the requirement. With the help of independent component analysis, the most dominating features are identified directly from UHF resonance signals without phase information. Using the identified features as the input, a neural network is implemented for recognizing sources of PD in SF6 and separating from the corona in air within a very short time. © 2006 IEEE.
Source Title: IEEE Transactions on Dielectrics and Electrical Insulation
URI: http://scholarbank.nus.edu.sg/handle/10635/82825
ISSN: 10709878
DOI: 10.1109/TDEI.2006.1667751
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