DocumentCode :
2070951
Title :
Identifying harmonic attributes from on-line partial discharge data
Author :
Catterson, V. ; Bahadoorsingh, S. ; Rudd, S. ; McArthur, S. ; Rowland, S.
Author_Institution :
Univ. of Strathclyde, Glasgow, UK
fYear :
2012
fDate :
22-26 July 2012
Firstpage :
1
Lastpage :
1
Abstract :
Summary form only given. Partial discharge (PD) monitoring is a key method of tracking fault progression and degradation of insulation systems. Recent research discovered that the harmonic regime experienced by the plant also affects the partial discharge pattern, questioning the conclusions about equipment health drawn from PD data. This paper presents the design and creation of an on-line system for harmonic circumstance monitoring of distribution cables, using only PD data. Based on machine learning techniques, the system can assess the prevalence of the 5th and 7th harmonic orders over the monitoring period. This information is key for asset managers to draw correct conclusions about the remaining life of polymeric cable insulation, and prevent overestimation of the degradation trend.
Keywords :
fault diagnosis; learning (artificial intelligence); partial discharges; polymer insulators; power cable insulation; power engineering computing; distribution cable harmonic circumstance monitoring; fault progression tracking; insulation systems; machine learning techniques; on-line partial discharge data monitoring; partial discharge pattern; polymeric cable insulation; Cable insulation; Degradation; Harmonic analysis; Monitoring; Partial discharges; Power cables;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting, 2012 IEEE
Conference_Location :
San Diego, CA
ISSN :
1944-9925
Print_ISBN :
978-1-4673-2727-5
Electronic_ISBN :
1944-9925
Type :
conf
DOI :
10.1109/PESGM.2012.6345761
Filename :
6345761
Link To Document :
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