Details
| Original language | English |
|---|---|
| Pages (from-to) | 69-74 |
| Number of pages | 6 |
| Journal | IEE Proceedings: Science, Measurement and Technology |
| Volume | 142 |
| Issue number | 1 |
| Publication status | Published - Jan 1995 |
Abstract
A method to separate partial discharges (PD) from pulse-shaped noise signals using a neural network is described. The structure of neural networks and their ability for pattern recognition is presented. The adaptive resonance theory (ART) architectures, which are suitable for PD measurement, and the fast simulating algorithm ART 2-A, are explained. It is shown that the ART 2-A network is able to classify pulses in accordance with their origin for the distribution transformer. An examination of the signals measured on a power transformer under high voltage on-site is presented.
ASJC Scopus subject areas
- Engineering(all)
- Electrical and Electronic Engineering
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In: IEE Proceedings: Science, Measurement and Technology, Vol. 142, No. 1, 01.1995, p. 69-74.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Separation of partial discharges from pulse-shaped noise signals with the help of neural networks
AU - Borsi, H.
AU - Gockenbach, Ernst
AU - Wenzel, D.
N1 - Copyright: Copyright 2004 Elsevier B.V., All rights reserved.
PY - 1995/1
Y1 - 1995/1
N2 - A method to separate partial discharges (PD) from pulse-shaped noise signals using a neural network is described. The structure of neural networks and their ability for pattern recognition is presented. The adaptive resonance theory (ART) architectures, which are suitable for PD measurement, and the fast simulating algorithm ART 2-A, are explained. It is shown that the ART 2-A network is able to classify pulses in accordance with their origin for the distribution transformer. An examination of the signals measured on a power transformer under high voltage on-site is presented.
AB - A method to separate partial discharges (PD) from pulse-shaped noise signals using a neural network is described. The structure of neural networks and their ability for pattern recognition is presented. The adaptive resonance theory (ART) architectures, which are suitable for PD measurement, and the fast simulating algorithm ART 2-A, are explained. It is shown that the ART 2-A network is able to classify pulses in accordance with their origin for the distribution transformer. An examination of the signals measured on a power transformer under high voltage on-site is presented.
UR - http://www.scopus.com/inward/record.url?scp=0029209562&partnerID=8YFLogxK
U2 - 10.1049/ip-smt:19951565
DO - 10.1049/ip-smt:19951565
M3 - Article
AN - SCOPUS:0029209562
VL - 142
SP - 69
EP - 74
JO - IEE Proceedings: Science, Measurement and Technology
JF - IEE Proceedings: Science, Measurement and Technology
SN - 1350-2344
IS - 1
ER -