E3S Web Conf.
Volume 81, 2019The 1st International Symposium on Water Resource and Environmental Management (WREM 2018)
|Number of page(s)||5|
|Published online||30 January 2019|
PRPD data analysis with Auto-Encoder Network
Electric Power Research Institute, State Grid Tianjin Electric Power Company, 300384 Tianjin, China
2 Red Phase INC., 361005 Xiamen, Fujian
* Corresponding author: email@example.com
Gas Insulated Switchgear (GIS) is related to the stable operation of power equipment. The traditional partial discharge pattern recognition method relies on expert experience to carry out feature engineering design artificial features, which has strong subjectivity and large blindness. To address the problem, we introduce an encoding-decoding network to reconstruct the input data and then treat the encoded network output as a partial discharge signal feature. The adaptive feature mining ability of the Auto-Encoder Network is effectively utilized, and the traditional classifier is connected to realize the effective combination of the deep learning method and the traditional machine learning method. The results show that the features extracted based on this method have better recognition than artificial features, which can effectively improve the recognition accuracy of partial discharge.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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