MATEC Web of Conferences
Volume 55, 20162016 Asia Conference on Power and Electrical Engineering (ACPEE 2016)
|Number of page(s)||6|
|Section||Fault Diagnostic and Fault-Tolerant Power Converters|
|Published online||25 April 2016|
Deep Learning for Intelligent Substation Device Infrared Fault Image Analysis
State Grid Shandong Electric Power Research Institute, Jinan, China
a Corresponding author: firstname.lastname@example.org
As an important kind of data for device status evaluation, the increasing infrared image data in electrical system puts forward a new challenge to traditional manually processing mode. To overcome this problem, this paper proposes a feasible way to automatically process massive infrared fault images. We take advantage of the imaging characteristics of infrared fault images and detect fault regions together with its belonging device part by our proposed algorithm, which first segment images into superpixels, and then adopt the state-of-the-art convolutional and recursive neural network for intelligent object recognition. In the experiment, we compare several unsupervised pre-training methods considering the importance of a pre-train procedure, and discuss the proper parameters for the proposed network. The experimental results show the good performance of our algorithm, and its efficiency for infrared analysis.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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