Deep neural networks for understanding and diagnosing partial discharge data

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 feat...

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Bibliographic Details
Main Authors: Catterson, V. M., Sheng, B.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Artificial neural networks
Biological neural networks
Computer architecture
deep learning
defects in oil
diagnostics
Machine learning
Neurons
partial discharge
Partial discharges
Training
UHF monitoring
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Summary: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.
ISSN:2334-0975
2576-6791
DOI:10.1109/ICACACT.2014.7223616