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An adaptive real-time disruption predictor for ASDEX Upgrade

An adaptive real-time disruption predictor for ASDEX Upgrade,10.1088/0029-5515/50/7/075004,Nuclear Fusion,B. Cannas,A. Fanni,G. Pautasso,G. Sias,P. So

An adaptive real-time disruption predictor for ASDEX Upgrade   (Citations: 3)
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In this paper, a neural predictor has been built using plasma discharges selected from two years of ASDEX Upgrade experiments, from July 2002 to July 2004. In order to test the real-time prediction capability of the system, its performance has been evaluated using discharges coming from different experimental campaigns, from June 2005 to July 2007. All disruptions that occurred in the chosen experimental campaigns were included with the exception of those occurring in the ramp-up phase, in the ramp-down phase (if the disruption does not happen in the first 100 ms), those caused by massive gas injection and disruptions following vertical displacement events. The large majority of selected disruptions are of the cooling edge type and typically preceded by the growth of tearing modes, degradation of the thermal confinement and enhanced plasma radiation. A very small percentage of them happen at large beta after a short precursor phase. For each discharge, seven plasma diagnostic signals have been selected from numerous signals available in real-time. During the training procedure, a self-organizing map has been used to reduce the database size in order to improve the training of the neural network. Moreover, an optimization procedure has been performed to discriminate between safe and pre-disruptive phases. The prediction success rate has been further improved, performing an adaptive training of the network whenever a missed alarm is triggered by the predictor.
Journal: Nuclear Fusion - NUCL FUSION , vol. 50, no. 7, 2010
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    • ...Among the more recent contributions we can cite [3], [4], [5], [6], [7], that refer to different tokamaks...
    • ...They have been selected on the basis of previous results presented in the literature [7], and taking into account physical considerations and the availability of real-time data...
    • ...As reconstructing the length of disruptive phase from unreliable or unavailable disruption precursor signals for each discharge could be a time consuming solution at AUG, in [7] the optimal value of tpre-disr = tD-45 ms, equal for all the training discharges, was set on a statistical basis...
    • ...In [7] it is shown that more than 90% of disruptive discharges has tLM within 160 ms from the disruption...
    • ...In this paper, for the sake of comparison with literature [7], the previous value of 160 ms is assumed, even if enlarging the successful prediction window will possibly bring to better performance...
    • ...In this paper, the performance of the prediction system IS evaluated in terms of [7]: • Successful Predictions (SPs): the fraction of plasma discharges (disruptive and/or safe) which are correctly predicted;...

    R. Aleddaet al. Mapping of the ASDEX Upgrade operational space for disruption predicti...

    • ...HE physical phenomena leading to disruptions are very complex and non-linear, and therefore no satisfactory model has been devised so far either for their avoidance, or their prediction [1]–[3]...

    M. Ruizet al. Real Time Plasma Disruptions Detection in JET Implemented With the ITM...

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