Title :
Neural network approach to separate aging and moisture from the dielectric response of oil impregnated paper insulation
Author :
Betie, A. ; Meghnefi, F. ; Fofana, I. ; Yeo, Z. ; Ezzaidi, H.
Author_Institution :
Dept. of Insulating Liquids & Mixed Dielectr. for Electrotechnol. (ISOLIME), Univ. du Quebec a Chicoutimi, Chicoutimi, QC, Canada
Abstract :
This paper presents a study of the impact of two important parameters, moisture and aging of the oil/paper dielectric used as insulation in power transformers.The way in which these two parameters influence different parameters of the Frequency Domain Spectroscopy (FDS) measurements, is emphasized.Different FDS parameters were measured by varying the moisturecontent and the aging degree of the oil impregnated paper.The use of two types of neural networks for analysis of the results was necessary in order to help discriminating the impact of moisture and aging on the FDS measurements and, in some cases, to estimate the aging duration of the paper impregnated with oil.
Keywords :
ageing; impregnated insulation; moisture; neural nets; paper; power engineering computing; power transformer insulation; transformer oil; FDS measurement; aging duration; frequency domain spectroscopy measurement; moisture content; neural network approach; oil impregnated paper insulation dielectric response; power transformer insulation; Aging; Dielectric measurement; Dielectrics; Frequency measurement; Insulation; Moisture; Moisture measurement; FDS; Power transformer; dielectric dissipation factor; neural networks; paper oil insulation; power factor;
Journal_Title :
Dielectrics and Electrical Insulation, IEEE Transactions on
DOI :
10.1109/TDEI.2015.004731
