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Since year 2010, the APODIS architecture has proven its accuracy predicting disruptions in JET tokamak. Nevertheless, it has shown margins for improvements, fact indisputable after the enhanced performances achieved in posterior upgrades. In this article, a complete optimization driven by Genetic Algorithms (GA) is applied to it aiming at considering all possible combination of signals, signal features,...
Disruption predictors are implemented as data-driven models obtained from machine learning techniques. These data-driven models are deduced from a training process with thousands of discharges (both disruptive and non-disruptive). ITER or DEMO, the next step devices cannot afford to wait for hundreds of disruptions to start predicting. A novelty approach for disruption prediction is to avoid the use...
This work analyses the recent evolution of statistical learning methods in JET for the prediction of disruptions. Disruption predictors are implemented as binary classification systems (two labels are possible: ‘disruptive’ and ‘non-disruptive’) whose training process is strongly related to the existence of disruption precursors in the signals. So far, the best predictors (in terms of success rate,...
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