DocumentCode :
3105903
Title :
Identification of aged cable section in 12.5 kV URD system based on Frequency Spectrum
Author :
Pushpanathan, B. ; Grzybowski, S. ; Bialek, T.O.
Author_Institution :
Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA
fYear :
2012
fDate :
7-10 May 2012
Firstpage :
1
Lastpage :
5
Abstract :
Underground residential distribution (URD) power cables are aged due to electrical, thermal, mechanical and environmental stress during their service. For utilities, the recent dielectric conditions of the cables are of much interest. Fast Fourier Transform Analysis of the impulse wave propagating in URD is one of the online non-destructive methods to estimate the cable insulation condition. In this study, the radial URD system with aged cable sections was modeled using EMTP for transient studies. A switching transient was simulated in the energized model of the URD system. Based on the Frequency Spectrum of the voltages at different points in the model URD, an aged cable section identification methodology using feature classification is presented in this paper.
Keywords :
EMTP; dielectric materials; fast Fourier transforms; feature extraction; pattern classification; power cable insulation; switching transients; underground residential distribution systems; EMTP; aged cable section identification; cable insulation condition; cable section identification methodology; cables dielectric conditions; electrical stress; environmental stress; fast Fourier transform analysis; feature classification; frequency spectrum; frequency spectrum-based URD system; impulse wave propagation; mechanical stress; online nondestructive methods; radial URD system; switching transient; thermal stress; underground residential distribution power cables; voltage 12.5 kV; Aging; Neural networks; Power cable insulation; Power cables; Transient analysis; Aged cable identification; Aged cable model; EMTP Simulation; Fast Fourier Transform; Frequency spectrum feature extraction; Heuristic Original Probabilistic Neural Network; On-line cable condition assessment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Transmission and Distribution Conference and Exposition (T&D), 2012 IEEE PES
Conference_Location :
Orlando, FL
ISSN :
2160-8555
Print_ISBN :
978-1-4673-1934-8
Electronic_ISBN :
2160-8555
Type :
conf
DOI :
10.1109/TDC.2012.6281656
Filename :
6281656
Link To Document :
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