NDLI: Deep neural networks for understanding and diagnosing partial discharge data

Content Provider	IEEE Xplore Digital Library
Author	Catterson, V.M. Sheng, B.
Copyright Year	2015
Description	Author affiliation: Inst. for Energy & Environ., Univ. of Strathclyde, Glasgow, UK (Catterson, V.M.; Sheng, B.)
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.
Starting Page	218
Ending Page	221
File Size	164206
Page Count	4
File Format	PDF
ISBN	9781479973521
e-ISBN	9781479973545
DOI	10.1109/ICACACT.2014.7223616
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Partial discharges Accuracy Biological neural networks Training Computer architecture Machine learning defects in oil Artificial neural networks deep learning partial discharge diagnostics UHF monitoring
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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