MHD spectrogram contribution to disruption prediction using Convolutional Neural Networks

The present research focuses on investigating deep neural networks techniques for predicting plasma disruptions in tokamaks. For this purpose, various deep-learning predictive models and several plasma diagnostics will be analyzed, using data gathered during the experimental campaigns conducted at the JET nuclear fusion tokamak between 2011 and 2020. The primary objective of this study is to show that the contribution of the MHD spectrograms increases the disruption predictor performance. The final deep-learning prediction model leverages the capability of Convolutional Neural Networks to directly learn important spatiotemporal information from 1D plasma profiles of temperature, density and power radiation, obtained from High Resolution Thomson Scattering and Bolometer diagnostics, as well as from spectrograms generated by a set of fast magnetic pick-up coils known as Mirnov coils. The Convolutional Neural Network eliminates the need for manual feature extraction methods that characterize the majority of machine learning methods. Using plasma profiles information allows to distinguish between core radiation caused by impurity accumulations and outboard radiation phenomena. Likewise, the decision to incorporate spectrograms from Mirnov coils is based on the diagnostic's ability to measure magnetic fluctuations originating from MHD instabilities, which can lead to disruptions. In order to address phenomena characterized by fast temporal dynamics, the inclusion of the locked mode signal was chosen. This signal is commonly employed at JET to trigger mitigation actions. It is integrated into an alarm scheme that employs both AND/OR logic and optimized thresholds, ensuring its effectiveness. The proposed predictor exhibits significant performance, with only one missed alarm out of 92 disrupted discharges and three false alarms out of 131 regularly terminated discharges in the test set.