Classification of tokamak plasma confinement states with convolutional recurrent neural networks

F. Matos (Corresponding author), V. Menkovski, F. Felici, A. Pau, F. Jenko

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

During a tokamak discharge, the plasma can vary between different confinement regimes: low (L), high (H) and, in some cases, a temporary (intermediate state), called dithering (D). In addition, while the plasma is in H mode, edge localized modes (ELMs) can occur. The automatic detection of changes between these states, and of ELMs, is important for tokamak operation. Motivated by this, and by recent developments in deep learning, we developed and compared two methods for automatic detection of the occurrence of L-D-H transitions and ELMs, applied on data from the TCV tokamak. These methods consist in a convolutional neural network and a convolutional long short term memory neural network. We measured our results with regards to ELMs using ROC curves and Youden's score index, and regarding state detection using Cohen's Kappa index.

Original languageEnglish
Article number036022
Number of pages16
JournalNuclear Fusion
Volume60
Issue number3
DOIs
Publication statusPublished - Mar 2020

Keywords

  • CNN
  • Deep learning
  • Dither
  • ELM
  • H mode
  • L mode
  • LSTM

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