Fault detection and health monitoring of high-power thyristor converter based on long short-term memory in nuclear fusion  

作　　者：Ling ZHANG Ge GAO Li JIANG 张玲;高格;蒋力

机构地区：[1]Institute of Plasma Physics,Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,People’s Republic of China [2]University of Science and Technology of China,Hefei 230026,People’s Republic of China

出　　处：《Plasma Science and Technology》2025年第4期64-73,共10页等离子体科学和技术(英文版)

基　　金：supported by the Open Fund of Magnetic Confinement Fusion Laboratory of Anhui Province(No.2024AMF04003);the Natural Science Foundation of Anhui Province(No.228085ME142);Comprehensive Research Facility for Fusion Technology(No.20180000527301001228)。

摘　　要：This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-term memory(LSTM)neural network model is proposed to monitor the operational state of the converter and accurately detect faults as they occur.By sampling and processing a large number of thyristor converter operation data,the LSTM model is trained to identify and detect abnormal state,and the power supply health status is monitored.Compared with traditional methods,LSTM model shows higher accuracy and abnormal state detection ability.The experimental results show that this method can effectively improve the reliability and safety of the thyristor converter,and provide a strong guarantee for the stable operation of the nuclear fusion reactor.

关 键 词：fault detection and health monitoring high-power supply thyristor converter long short-term memory(LSTM) nuclear fusion(Some figures may appear in colour only in the online journal) 

分 类 号：TM46[电气工程—电器]