Simultaneous Detection of Radial Deformation Location and Axial Displacement Extent in a Transformer Winding Using Cross-correlation Technique and ANN_1
Research Area: | Volume 1 Issue 5, Sept. 2012 | Year: | 2012 |
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Type of Publication: | Article | Keywords: | Radial Deformation RD, Axial Displacement AD, Feature selection, Artificial Neural Network ANN |
Authors: |
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Journal: | IJEIR | Volume: | 1 |
Number: | 5 | Pages: | 478-483 |
Month: | Sept. | ||
ISSN: | 2277-5668 | ||
Abstract: | In this paper, a new method to detect and
locate mechanical defects, as one of the major faults in power
transformer windings, is proposed. Axial displacement and
radial deformation are considered as two major types of
mechanical defects in this study. In the first step, a real
transformer winding is modelled based on detailed model
considering geometrical specifications and the frequency
response characteristics are obtained for intact and defected
modes. This technique is called Frequency Response Analysis
(FRA). Thereafter, some features based on cross-correlation
and other mathematical patterns are selected from the
obtained signals. These features are then used to train an
ANN classifier. The proposed method is able to discriminate
between radial deformation and axial displacement defects
precisely and determine the axial displacement extent and
radial deformation location with a good accuracy. This
accuracy rate reaches 81.25% for axial displacement extent
and 75% for radial deformation location. |
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Full text: IJEIR-289 FINAL.pdf
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