Identification of Partial Discharge State of Transformer Based on Cov-Iradon TransformationCNN

被引：0

作者：

Li Le ^{[1
]}

Li Chaoran ^{[1
]}

Liu Zhiyuan ^{[1
]}

Han Hao ^{[1
]}

Zhang Ying ^{[1
]}

Zhu Xiaoxun ^{[2
]}

机构：

[1] State Grid Beijing Daxing power supply Co, Sales Dept, Beijing, Peoples R China

[2] North China Elect Power Univ, Dept power Engn, Baoding, Hebei, Peoples R China

来源：

2024 7TH ASIA CONFERENCE ON ENERGY AND ELECTRICAL ENGINEERING, ACEEE 2024 | 2024年

关键词：

transformer; partial discharge; covariance; inverse radon transform; convolutional neural network;

D O I：

10.1109/ACEEE62329.2024.10651618

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

As an important part of power system, the discharge phenomenon caused by insulation deterioration of power transformer may cause equipment damage. In this paper, corona discharge, air gap discharge and suspension discharge are studied experimentally. Firstly, Covariance (Cov) is used to highlight signal period information and reduce signal noise. Secondly, the inverse Radon transform (Iradon) is used to convert one-dimensional data into two-dimensional images to highlight the characteristic difference between different discharge signals. Finally, convolutional neural network (CNN) is used for state recognition, and the accuracy rate reaches 96.03%.

引用

页码：24 / 28

页数：5

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