A prediction tool for real-time application in the disruption protection system at JET

Abstract

A disruption prediction system, based on neural networks, is presented in this paper. The system is ideally suitable for on-line application in the disruption avoidance and/or mitigation scheme at the JET tokamak.A multi-layer perceptron (MLP) predictor module has been trained on nine plasma diagnostic signals extracted from 86 disruptive pulses, selected from four years of JET experiments in the pulse range 47830–57346 (from 1999 to 2002).The disruption class of the disruptive pulses is available. In particular, the selected pulses belong to four classes (density limit/high radiated power, internal transport barrier, mode lock and h-mode/l-mode).A self-organizing map has been used to select the samples of the pulses to train the MLP predictor module and to determine its target, increasing the prediction capability of the system.The prediction performance has been tested over 86 disruptive and 102 non-disruptive pulses. The test has been performed presenting to the network all the samples of each pulse sampled every 20 ms. The missed alarm rate and the false alarm rate of the predictor, up to 100 ms prior to the disruption time, are 23% and 1%, respectively.Recent plasma configurations might present features different from those observed in the experiments used in the training set. This ‘novelty’ can lead to incorrect behaviour of the predictor. To improve the robustness and reliability of the system, a novelty detection module has been integrated in the prediction system, increasing the system performance and resulting in a missed alarm rate reduced to 7% and a false alarm rate reduced to 0%.

Event recognition using signal spectrograms in long pulse experiments

“…With the increase in the duration of discharge time, proposals able to work in real-time become more relevant. Various approaches are based on artificial neural networks, 5 support vector machines, 6 or fuzzy logic combined with regression trees. 7 These approaches not only highlight the importance of characterizing and classification of signals but also make it possible to detect events associated with the results of the classification in real-time.…”

Detection of Disruptions in the High-β Spherical Torus NSTX

“…Neural networks are typically trained, in the sense that a predetermined sample of input and output data are used to determine the optimal values of the coefficients of the network. Neural networks have been used to predict various forms of disruptions on ASDEX Upgrade [84][85][86], DIII-D [87], ADITYA [88,89], TEXT [90,91], JET [85,92,93], and JT-60 [94,95].…”

“…Typical input data to the network include a measure of the plasma β (the normalized β (β N ) [96,97] or poloidal β (β P )), edge safety factor, plasma density (or Greenwald fraction [98,99]), locked mode amplitude, input power, radiated power, shape parameters, internal inductance, confinement time, and neutron emission. Some early work also used soft X-ray emission [87][88][89]91], high(er) frequency magnetic probes [87][88][89][90], or Dα monitors [87][88][89] though these diagnostics have typically not been used in more recent studies [85,86,92,93,95].…”

“…For instance, Ref. [93] shows missed and false alarm rates of 23% and 1% respectively from a sample of 86 disrupting pulses and 102 non-disrupting pulses on the JET tokamak. Furthermore, initial cross-machine benchmarking has been completed.…”

Desenvolvimento e utilização de um injetor de pastilhas de impurezas no estudo da mitigação de disrupções e atenuação de raios-X de altas energias

“…Uma técnica interessante que já foi empregada utiliza redes neurais para a previsão do momento da disrupção [34][35][36][37].…”