Abstract
This paper compares the ability of a Single Neural Network (SNN) and an Ensemble Neural Network (ENN) in classifying and discriminating oil-pressboard interface partial discharge (PD) degredation. Discharges were sustained for 15 hours and PD patterns captured, evaluated and correlated with the tracking damage on the pressboard surface. For the same experimental arrangement two samples were stressed, one at 18.5kV (rms) and the other at 30kV (rms).
Original language | English |
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Title of host publication | Proceedings of the 2012 IEEE International Symposium on Electrical Insulation (ISEI) |
Subtitle of host publication | Conference Record of the 2012 IEEE International Symposium on Electrical Insulation (ISEI 2012) |
Publisher | IEEE |
Pages | 122-126 |
Number of pages | 5 |
ISBN (Electronic) | 9781467304863 |
ISBN (Print) | 9781467304887 |
DOIs | |
Publication status | Published - 2012 |
Event | 2012 IEEE International Symposium on Electrical Insulation (ISEI 2012) - San Juan, Puerto Rico, United States Duration: 10 Jun 2012 → 13 Jun 2012 |
Conference
Conference | 2012 IEEE International Symposium on Electrical Insulation (ISEI 2012) |
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Country/Territory | United States |
City | San Juan, Puerto Rico |
Period | 10/06/12 → 13/06/12 |
Keywords
- surface tracking
- high voltage
- ensemble neural networks
- single neural network