Fault Diagnosis of UHVDC Transmission Line Based on Deep Neural Network

被引：3
|
作者：
Wang, Lei [1 ]
Zhao, Qingsheng [1 ]
Liang, Dingkang [1 ]
机构：
[1] Taiyuan Univ Technol, Shanxi Key Lab Power Syst, Operat & Control Coll, Taiyuan, Peoples R China
来源：
2022 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (I&CPS ASIA 2022) | 2022年
关键词：
UHVDC Transmission Lines; Fault Diagnosis; Convolutional Neural Network; Gated Recurrent Unit; Deep Learning; SYSTEM;
D O I：
10.1109/ICPSAsia55496.2022.9949678
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：
Existing ultra high voltage direct current (UHVDC) fault detection methods have low sensitivity and are difficult to identify high resistance ground faults. A fault diagnosis method for UHVDC transmission system based on the combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) is proposed. In this method, 16 electrical signals such as DC line voltage and DC line current are taken as the input of the fault diagnosis model, and then the features are extracted adaptively through the convolutional neural network, and the original features are dimensionally reduced. Finally, the new features are classified by the gated cyclic element network. The +/- 800kV UHVDC transmission line model was built by MATLAB/Simulink simulation software, and simulation experiments were conducted on different fault areas and fault types. The test results show that the proposed fault diagnosis method can reliably and accurately identify various internal and external faults of UHVDC transmission lines, and has a strong ability to withstand transition resistance.
引用
收藏
页码：445 / 450
页数：6
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