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Neural network based diagnosis of partial discharge defects patterns at XLPE cable under DC stress

Abstract
The application of the XLPE cable under DC stress is now being considered as one of the promising tool for enlarging the limited transmission capacity of the traditional electric power cable due to the several technical advantages. Therefore, many research institutes are trying to improve the performance by carrying out experimental investigations related to the reliability of the cable to be used
under DC stress. One of them is partial discharge (PD) detection, which is considered as the tool to indicate the insulation state of the apparatus; however, very few reports have been published based on the results obtained for the XLPE cable used under the DC stress. In this work, three different types of artificial defects are designed to produce PD under DC: void, surface and corona discharge defects. Pattern recognition of the detected PD signal is based on modified CAPD method.To improve thePDpattern recognition rate, the training
data obtained through neural networks is combined with the results of the frequency domain analysis. Finally, the training data and mean square error of PD patterns obtained through neural networks is compared with the training data obtained with the use of only modified CAPD method.

Keywords: Partial discharge (PD) · Chaotic analysis of partial discharge (CAPD) · Artificial neural networks
(ANN) · Multilayer perceptron (MLP) · Cross-linked polyethylene (XLPE) cable

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Informasi Detail
Judul Seri
-
No. Panggil
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Penerbit
: Springer-Verlag Berlin Heidelberg.,
Deskripsi Fisik
Electr Eng (2017) 99:119–132
Bahasa
English
ISBN/ISSN
1432-0487
Klasifikasi
NONE
Tipe Isi
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Tipe Media
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Tipe Pembawa
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Edisi
Electronical engineering ; Volume 99, Issue 1, Mar
Subjek
TEKNIK ELEKTRO
ELECTRICAL ENGINEERING
Info Detail Spesifik
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Pernyataan Tanggungjawab
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