期刊
IEEE TRANSACTIONS ON PLASMA SCIENCE
卷 48, 期 1, 页码 54-60出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPS.2019.2951367
关键词
Neural networks; plasma measurements; real-time plasma shape reconstruction
资金
- National Magnetic Confinement Fusion Energy R&D Program of China [2018YFE0302100]
- National Natural Science Foundation of China [11575245, 11805236, 11905256]
- National Key Research and Development Program of China [2017YFE0300500]
- Young and Middle-Aged Academic Back-Bone Finance Fund from Anhui Medical University
For the efficient and safe operation of experimental advanced superconducting tokamak (EAST), it is necessary to accurately identify and control the plasma current and its central position. In this article, neural network is used to identify the position of the plasma current center. The model trained by the basic back-propagation neural network can well match the relationship between the electromagnetic diagnostic signals and plasma current center positions. Both noisy simulation data and experimental data are applied to train and test the neural network inference model. Adding 0.1% noise to the training data is proven to improve the noise immunity of the inference model. Basic neural networks trained with both noisy simulation data and actual experimental data show good results with sufficient inputs; however, in both cases, the performance degrades significantly when only the poloidal field coil currents are given as inputs. For this kind of time-series problem, the dynamic neural network containing delay and feedback architecture is introduced, and an improved model requiring much fewer inputs is trained and tested for current center inference. Some parameters of this model are compared and analyzed in this article. With suitable neural network architecture, the mapping between the controlled variables (poloidal field coil currents) and response variables (plasma current center) can be well-established.
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