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Disruption prediction and model analysis using LightGBM on J-TEXT and HL-2A

文献类型：期刊论文

作者Zhong,Y1; Zheng,W1; Chen,Z Y1,5; Xia,F4; Yu,L M4; Wu,Q Q1; Ai,X K1; Shen,C S1; Yang,Z Y4; Yan,W1
刊名Plasma Physics and Controlled Fusion
出版日期2021-05-21
卷号63
关键词disruption prediction machine learning LightGBM feature importance
ISSN号0741-3335
DOI10.1088/1361-6587/abfa74
通讯作者Zheng,W() ; Chen,Z Y()
英文摘要AbstractUsing machine learning (ML) techniques to develop disruption predictors is an effective way to avoid or mitigate the disruption in a large-scale tokamak. The recent ML-based disruption predictors have made great progress regarding accuracy, but most of them have not achieved acceptable cross-machine performance. Before we develop a cross-machine predictor, it is very important to investigate the method of developing a cross-tokamak ML-based disruption prediction model. To ascertain the elements which impact the model’s performance and achieve a deep understanding of the predictor, multiple models are trained using data from two different tokamaks, J-TEXT and HL-2A, based on an implementation of the gradient-boosted decision trees algorithm called LightGBM, which can provide detailed information about the model and input features. The predictor models are not only built and tested for performance, but also analyzed from a feature importance perspective as well as for model performance variation. The relative feature importance ranking of two tokamaks is caused by differences in disruption types between different tokamaks. The result of two models with seven inputs showed that common diagnostics is very important in building a cross-machine predictor. This provided a strategy for selecting diagnostics and shots data for developing cross-machine predictors.
WOS关键词TOOL
资助项目National Key Research and Development Program of China[2017YFE0301202] ; National Key Research and Development Program of China[2017YFE0300500] ; National Key Research and Development Program of China[2017YFE0300501] ; National Magnetic Confinement Fusion Science Program[2015GB111002] ; National Magnetic Confinement Fusion Science Program[2015GB104000] ; National Natural Science Foundation of China[11775089] ; National Natural Science Foundation of China[51821005] ; National Natural Science Foundation of China[71762031] ; National Natural Science Foundation of China[11875022] ; National Natural Science Foundation of China[11905077] ; National Natural Science Foundation of China[11575068] ; National MCF Energy R&D Program of China[2019YFE03010004] ; China Postdoctoral Science Foundation[2019M652615]
WOS研究方向Physics
语种英语
WOS记录号IOP:0741-3335-63-7-ABFA74
出版者IOP Publishing
资助机构National Key Research and Development Program of China ; National Magnetic Confinement Fusion Science Program ; National Natural Science Foundation of China ; National MCF Energy R&D Program of China ; China Postdoctoral Science Foundation
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/122528]  
专题中国科学院合肥物质科学研究院
通讯作者Zheng,W; Chen,Z Y
作者单位1.International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics, State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, People’s Republic of China
2.Forschungszentrum Jülich GmbH, Institut für Energie- und Klimaforschung—Plasmaphysik, 52425 Jülich, Germany
3.Institute of Plasma Physics, Chinese Academy of Sciences, PO Box 1126, Hefei 230031, People’s Republic of China
4.Southwestern Institute of Physics, Chengdu 610041, People’s Republic of China
5.Chengdu University, 610106 Chengdu, People’s Republic of China
推荐引用方式
GB/T 7714		 Zhong,Y,Zheng,W,Chen,Z Y,et al. Disruption prediction and model analysis using LightGBM on J-TEXT and HL-2A[J]. Plasma Physics and Controlled Fusion,2021,63.
APA Zhong,Y.,Zheng,W.,Chen,Z Y.,Xia,F.,Yu,L M.,...&Li,F.(2021).Disruption prediction and model analysis using LightGBM on J-TEXT and HL-2A.Plasma Physics and Controlled Fusion,63.
MLA Zhong,Y,et al."Disruption prediction and model analysis using LightGBM on J-TEXT and HL-2A".Plasma Physics and Controlled Fusion 63(2021).

入库方式： OAI收割

来源：合肥物质科学研究院

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