A multidimensional linear model for disruption prediction in JET

Abstract

The implementation of Machine Learning (ML) techniques has considerably improved the prediction of disruptions. However, they usually provide outcomes difficult to understand from a physics point of view due to their mathematical formulation. In this work an interpretable linear equation has been derived from an accurate ML disruption predictor. It can be used for real-time forecasting and the off-line analysis of the variables that contribute to the alarm triggering. To create the linear model, in addition to physic quantities, Time Increments (TIs) have been considered. TIs represent the variation of two amplitude values of a signal X at two different times divided by their temporal difference (i.e. ΔX/Δt). To select the best subset of quantities for training purposes among the wide possible combinations of signals and Tis, Genetic Algorithms have been applied. The results, obtained over an independent testing database of 131 unintentional disruptive and 1310 non-disruptive shots, are 99,24% of success rate (94,66% of them with at least 10 ms of warning time) and 3,51% of false alarms.