DocumentCode :
985523
Title :
Application of extension theory to PD pattern recognition in high-voltage current transformers
Author :
Wang, Mang-Hui ; Ho, Chih-Yung
Author_Institution :
Inst. of Inf. & Electr. Energy, Nat. Chin-Yi Inst. of Technol., Taichung, Taiwan
Volume :
20
Issue :
3
fYear :
2005
fDate :
7/1/2005 12:00:00 AM
Firstpage :
1939
Lastpage :
1946
Abstract :
This paper presents a novel partial-discharge (PD) recognition method based on the extension theory for high-voltage cast-resin current transformers (CRCTs). First, a commercial PD detector is used to measure the three-dimensional (3-D) PD patterns of the high-voltage CRCTs, then three data preprocessing schemes that extract relevant features from the raw 3-D-PD patterns are presented for the proposed PD recognition method. Second, the matter-element models of the PD defect types are built according to PD patterns derived from practical experimental results. Then, the PD defect in a CRCT can be directly identified by degrees of correlation between the tested pattern and the matter-element models which have been built up. To demonstrate the effectiveness of the proposed method, comparative studies using a multilayer neural network and k-means algorithm are conducted on 150 sets of field-test PD patterns of 23-kV CRCTs with rather encouraging results.
Keywords :
current transformers; feature extraction; neural nets; partial discharges; power engineering computing; transformer testing; data preprocessing schemes; extension theory; feature extraction; high-voltage cast-resin current transformer; k-means algorithm; matter-element models; multilayer neural network; partial discharge pattern recognition; Current transformers; Data mining; Data preprocessing; Detectors; Feature extraction; Multi-layer neural network; Neural networks; Partial discharges; Pattern recognition; Testing; Current transformers (CTs); extension theory; matter-element model; partial discharge (PD);
fLanguage :
English
Journal_Title :
Power Delivery, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8977
Type :
jour
DOI :
10.1109/TPWRD.2005.848673
Filename :
1458864
Link To Document :
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