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Special Issue:
SPECIAL TOPIC — Plasma disruption
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| SPECIAL TOPIC—Plasma disruption |
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Disruption prediction based on fusion feature extractor on J-TEXT |
| Wei Zheng(郑玮)1, Fengming Xue(薛凤鸣)1,2, Zhongyong Chen(陈忠勇)1,†, Chengshuo Shen(沈呈硕)1, Xinkun Ai(艾鑫坤)1, Yu Zhong(钟昱)1, Nengchao Wang(王能超)1, Ming Zhang(张明)1, Yonghua Ding(丁永华)1, Zhipeng Chen(陈志鹏)1, Zhoujun Yang(杨州军)1, and Yuan Pan(潘垣)1 |
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, China;
2 Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074, China |
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Abstract Predicting disruptions across different tokamaks is necessary for next generation device. Future large-scale tokamaks can hardly tolerate disruptions at high performance discharge, which makes it difficult for current data-driven methods to obtain an acceptable result. A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the problem. The key is a feature extractor which is able to extract common disruption precursor traces in tokamak diagnostic data, and can be easily transferred to other tokamaks. Based on the concerns above, this paper presents a deep feature extractor, namely, the fusion feature extractor (FFE), which is designed specifically for extracting disruption precursor features from common diagnostics on tokamaks. Furthermore, an FFE-based disruption predictor on J-TEXT is demonstrated. The feature extractor is aimed to extracting disruption-related precursors and is designed according to the precursors of disruption and their representations in common tokamak diagnostics. Strong inductive bias on tokamak diagnostics data is introduced. The paper presents the evolution of the neural network feature extractor and its comparison against general deep neural networks, as well as a physics-based feature extraction with a traditional machine learning method. Results demonstrate that the FFE may reach a similar effect with physics-guided manual feature extraction, and obtain a better result compared with other deep learning methods.
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Received: 29 December 2022
Revised: 24 March 2023
Accepted manuscript online: 28 March 2023
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PACS:
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52.55.Fa
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(Tokamaks, spherical tokamaks)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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| Fund: Project supported by the National Key R&D Program of China (Grant No. 2022YFE03040004) and the National Natural Science Foundation of China (Grant No. 51821005). |
Corresponding Authors:
Zhongyong Chen
E-mail: zychen@hust.edu.cn
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Cite this article:
Wei Zheng(郑玮), Fengming Xue(薛凤鸣), Zhongyong Chen(陈忠勇), Chengshuo Shen(沈呈硕), Xinkun Ai(艾鑫坤), Yu Zhong(钟昱), Nengchao Wang(王能超), Ming Zhang(张明),Yonghua Ding(丁永华), Zhipeng Chen(陈志鹏), Zhoujun Yang(杨州军), and Yuan Pan(潘垣) Disruption prediction based on fusion feature extractor on J-TEXT 2023 Chin. Phys. B 32 075203
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