Deep neural networks for understanding and diagnosing partial discharge data
Catterson, V. M. and Sheng, B.; (2015) Deep neural networks for understanding and diagnosing partial discharge data. In: 2015 IEEE Electrical Insulation Conference (EIC). IEEE, USA, pp. 218-221. ISBN 9781479973521 (https://doi.org/10.1109/ICACACT.2014.7223616)
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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.
ORCID iDs
Catterson, V. M. ORCID: https://orcid.org/0000-0003-3455-803X and Sheng, B. ORCID: https://orcid.org/0000-0002-2878-7066;-
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Item type: Book Section ID code: 53898 Dates: DateEvent7 June 2015Published14 November 2014AcceptedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 28 Jul 2015 10:45 Last modified: 22 Sep 2024 00:36 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/53898