title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÑÑÐ²Ð° Ð² Ð¿Ð»Ð°Ð·Ð¼Ðµ ÑÐ¾ÐºÐ°Ð¼Ð°ÐºÐ° Â«ÐÐ»Ð¾Ð±ÑÑ-Ð/Ð2Â» Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Study of disruption in plasma of the Â«Globus-M/M2Â» tokamak using neural networks
Creators: name: ÐÐ¾Ð³Ð´Ð°Ð½Ð¾Ð², ÐÐ½ÑÐ¾Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ðµ ÑÑÑÐ²Ð¾Ð²
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ð¾Ð½Ð½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð·
disruption prediction
deep machine learning
correlation

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description: This work is devoted to the study of the disruption prediction in the plasma of the Globus-M/M2 tokamak using deep machine learning. Disruptions are strictly undesirable events for a reactor-scale tokamak; therefore, it is necessary to either reduce the frequency of disruptions or mitigate the consequences of disruptions that cannot be avoided by other means. To mitigate the consequences of the breakdown, it is necessary to form a trigger of this event ahead of time sufficient to activate the controlled discharge extinguishing system. The purpose of this work is to investigate the possibility of using neural networks for predicted events in the plasma of the Globus-M2 tokamak using data on disruptions as an example. To achieve this goal, a correlation analysis of signals from diagnostics available in real time was carried out in order to select a set of signals used to predict breakdown. A neural network topology with parallel convolutional and fully connected layers is chosen. A software package has been developed for identifying a basic set of signals, preparing training examples, training a neural network model and evaluating the resulting efficiency. The efficiency of the system has been tested with 50 test discharges. The value of false alarms for the model trained in this work was 14%, the value of missed disruptions was 7%.