DocumentCode :
1856361
Title :
Deep neural networks for understanding and diagnosing partial discharge data
Author :
Catterson, V.M. ; Sheng, B.
Author_Institution :
Inst. for Energy & Environ., Univ. of Strathclyde, Glasgow, UK
fYear :
2015
fDate :
7-10 June 2015
Firstpage :
218
Lastpage :
221
Abstract :
Artificial neural networks have been investigated for many years as a technique for automated diagnosis of defects causing partial discharge (PD). While good levels of accuracy have been reported, disadvantages include the difficulty of explaining results, and the need to hand-craft appropriate features for standard two-layer networks. Recent advances in the design and training of deep neural networks, which contain more than two layers of hidden neurons, have resulted in improved results in speech and image recognition tasks. This paper investigates the use of deep neural networks for PD diagnosis. Defect samples constructed in mineral oil were used to generate data for training and testing. The paper demonstrates the improvements in accuracy and visualization of learning which can be gained from deep learning.
Keywords :
data analysis; electrical maintenance; fault diagnosis; learning (artificial intelligence); neural nets; partial discharges; power engineering computing; deep learning; deep neural networks; hidden neurons; mineral oil; partial discharge data; partial discharge diagnosis; Accuracy; Biological neural networks; Computer architecture; Machine learning; Neurons; Partial discharges; Training; Artificial neural networks; UHF monitoring; deep learning; defects in oil; diagnostics; partial discharge;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Insulation Conference (EIC), 2015 IEEE
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4799-7352-1
Type :
conf
DOI :
10.1109/ICACACT.2014.7223616
Filename :
7223616
Link To Document :
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