Neural Network Prediction of Disruptions Caused by Locked Modes on J-TEXT Tokamak

DING Yonghua (丁永华); JIN Xuesong (金雪松); CHEN Zhenzhen (陈真真); ZHUANG Ge (庄革)

doi:10.1088/1009-0630/15/11/14

Plasma Science and Technology > 2013 > 15(11): 1154-1159. > DOI: 10.1088/1009-0630/15/11/14

DING Yonghua (丁永华), JIN Xuesong (金雪松), CHEN Zhenzhen (陈真真), ZHUANG Ge (庄革). Neural Network Prediction of Disruptions Caused by Locked Modes on J-TEXT Tokamak[J]. Plasma Science and Technology, 2013, 15(11): 1154-1159. DOI: 10.1088/1009-0630/15/11/14

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Neural Network Prediction of Disruptions Caused by Locked Modes on J-TEXT Tokamak

State Key Laboratory of Advanced Electromagnetic Engineering and Technology, College of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Funds: supported by the National Magnetic Confinement Fusion Science Program of China (Nos.2010GB107004, 2011GB109001, 2008CB717805) and National Natural Science Foundation of China (Nos.11275080, 10935004)

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Received Date: March 17, 2011

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Abstract

Abstract

Prediction of disruptions caused by locked modes using the Back-Propagation (BP) neural network is completed on J-TEXT tokamak. The network, which is based on the BP neural network, uses Mirnov coils and locked mode coils signals as input data, and outputs a signal including information of prediction of locked mode. The rate of successful prediction of locked modes is more than 90%. For intrinsic locked mode disruptions, the network can give a prewarning signal about 1 ms ahead of the locking-time. For the disruption caused by resonant magnetic perturbation (RMPs) locked modes, the network can give a prewarning signal about 10 ms ahead of the locking-time.
- disruption,
- locked mode,
- BP neural network,
- prediction

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[6]	Chengxu LU (吕程序), Bo WANG (王博), Xunpeng JIANG (姜训鹏), Junning ZHANG (张俊宁), Kang NIU (牛康), Yanwei YUAN (苑严伟). Detection of K in soil using time-resolved laser-induced breakdown spectroscopy based on convolutional neural networks[J]. Plasma Science and Technology, 2019, 21(3): 34014-034014. DOI: 10.1088/2058-6272/aaef6e
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[10]	CHEN Junjie (陈均杰), LI Guoqiang (李国强), QIAN Jinping (钱金平), LIU Zixi (刘子奚). Ideal MHD Stability Prediction and Required Power for EAST Advanced Scenario[J]. Plasma Science and Technology, 2012, 14(11): 947-952. DOI: 10.1088/1009-0630/14/11/01