文章基本信息

标题：PRPD data analysis with Auto-Encoder Network
本地全文：下载
作者：Songyuan Li ; Yuyan Man ; Chi Zhang 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2019
卷号：81
页码：1-5
DOI：10.1051/e3sconf/20198101019
出版社：EDP Sciences
摘要：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.
其他摘要：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.