Title :
Identifying Harmonic Attributes From Online Partial Discharge Data
Author :
Catterson, Victoria M. ; Bahadoorsingh, Sanjay ; Rudd, Susan ; McArthur, Stephen D J ; Rowland, Simon M.
Author_Institution :
Inst. for Energy & Environ., Univ. of Strathclyde, Glasgow, UK
fDate :
7/1/2011 12:00:00 AM
Abstract :
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 PD pattern, questioning the conclusions about equipment health drawn from PD data. This paper presents the design and creation of an online 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 :
learning (artificial intelligence); partial discharges; power cable insulation; power engineering computing; power system faults; power system harmonics; power system measurement; PD pattern; distribution cables; fault progression tracking; harmonic attribute identification; harmonic circumstance monitoring; insulation system degradation; machine learning techniques; online partial discharge data; partial discharge monitoring; polymeric cable insulation; Discharges; Harmonic analysis; Insulation; Monitoring; Partial discharges; Power system harmonics; Training; Dielectric breakdown; epoxy resin insulation; monitoring; partial discharge (PD); power quality (PQ); power system harmonics;
Journal_Title :
Power Delivery, IEEE Transactions on
DOI :
10.1109/TPWRD.2011.2114373
