Identifying defects in SF6:N2 mixtures using external UHF couplers and Neural Network analysis
No Thumbnail Available
Date
2011-10-21
Authors
Govender, Thavenesen
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
An external measurement system capable of recording Partial Discharge (PD)
Ultra High Frequency (UHF) signatures at a measurement frequency of 1 GHz
from defects in Gas Insulated Substations (GIS) has been developed. Phase
resolved PD patterns for a 15 mm metallic protrusion on the HV bus bar and
15 mm free conducting particle were obtained for SF6:N2 mixtures containing
10:90, 20:80 and 100:0 SF6:N2 content. Simple statistical features such as dis-
charge occurrence and mean magnitude were extracted from the phase resolved
plots and used as inputs into an Artificial Neural Network (ANN) for the clas-
sification of defects. A comparison between defect signatures in the different
mixtures is made and an investigation into the feasibility of using data taken
in pure SF6 to aid in classifying defects in SF6:N2 mixtures with an ANN is
undertaken. The overall classification accuracy of the ANN built and trained
on pure SF6 data for the statistical features of discharge occurrence and mean
value is 82 % for the two defects in the three different mixtures. However, a
10 % accuracy was obtained for the free particle in the 20:80 SF6:N2 mixture. A second method of extracting statistical features from PD signatures is de-
veloped. Statistical features of skewness, kurtosis and variance are computed
on the positive and negative half cycles of the phase resolved Maximum En-
velope and are used as inputs into the ANN. This ANN achieved an overall
classification accuracy of 96.5 % in discriminating between a protrusion on the
HV conductor and a free particle in 10:90, 20:80 and 100:0 SF6:N2 mixtures.
A 71 % improvement in classification accuracy for a free particle in the 20:80
SF6:N2 mixture is obtained as compared to the orginal ANN using features
such as discharge occurrence and mean value of discharges computed on the
positive and negative half cycles. The feasibility of using defect signatures
taken in pure SF6 for classifying defects in SF6:N2 mixtures is shown. The
effect of evolving PD signatures as well as the suitability of statistical features
to be used for classification is discussed.