Disruption prediction using a full convolutional neural network on EAST

Abstract

In this study, a full convolutional neural network is trained on a large database of experimental EAST data to classify disruptive discharges and distinguish them from non-disruptive discharges. The database contains 14 diagnostic parameters from the ∼104 discharges (disruptive and non-disruptive). The test set contains 417 disruptive discharges and 999 non-disruptive discharges, which are used to evaluate the performance of the model. The results reveal that the true positive (TP) rate is ∼ 0.827, while the false positive (FP) rate is ∼0.067. This indicates that 72 disruptive discharges and 67 non-disruptive discharges are misclassified in the test set. The FPs are investigated in detail and are found to emerge due to some subtle disturbances in the signals, which lead to misjudgment of the model. Therefore, hundreds of non-disruptive discharges from training set, containing time slices of small disturbances, are artificially added into the training database for retraining the model. The same test set is used to assess the performance of the improved model. The TP rate of the improved model increases up to 0.875, while its FP rate decreases to 0.061. Overall, the proposed data-driven predicted model exhibits immense potential for application in long pulse fusion devices such as ITER.

Research on plasma vertical displacement calculation based on neural network

“…To ensure model accuracy, the database should contain sufficient sample data for all the different configurations. Further, one single shot on EAST contains several diagnosis signals (Guo, Chen & Shen 2021; Guo, Shen & Chen 2021). Of these, only 88 vertical displacement control signals are chosen, containing 14 poloidal field (PF) coil current signals, 35 magnetic flux (FL) loops, 38 magnetic probes and plasma current.…”

Disruption prediction with artificial intelligence techniques in tokamak plasmas

“…Approaches relying on a mixture of time/frequency domains, including wavelet decompositions, have also been pursued [17][18][19] . With regard to classifier technologies, real-time compatible predictors have typically been based on artificial neural networks, support vector machines, fuzzy logic, generative topographic mapping and deep learning and have been studied on a broad range of tokamaks, including ADITYA (India) 20 , ASDEX Upgrade (Germany) 21 , DIII-D (United States) [22][23][24] , J-TEXT (China) 25 , NSTX (United States) 26 , ALCATOR C-MOD (United States) 27 , JT-60U (Japan) 28 , EAST (China) [29][30][31] , HL-2A (China) 32 and JET (United Kingdom) [33][34][35] .…”

“…The classifiers employed in the studies on tokamaks mentioned above [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] were developed using real-time valid solutions, which guarantee response times within a specified time window. The predictors discussed in the remainder of this work have been tested offline with real-time compatible technologies and using only real-time available signals.…”

Disruption prediction based on fusion feature extractor on J-TEXT

“…[12] EAST has developed disruption predictors based on CNN and LSTM, separately. [13,14] Fusion recurrent neural network (FRNN) has also been developed and has been applied to JET and DIII-D. [15] Though data-driven methods do not necessarily need to manually extract features, it is important to combine proper characteristics of disruption precursors. All deep learning-based methods mentioned above are designed with comprehension of disruption precursors.…”