Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time

Abstract

This article describes the use of privileged information to train supervised classifiers, applied for

the first time to the prediction of disruptions in tokamaks. The objective consists of making

predictions with real-time signals during the discharges (as usual) but after training the predictor

also with any kind of data at training time that is not available during discharge execution. The

latter kind of data is known as privileged information. Taking into account the limited number

of foreseen real time signals for disruption prediction at the beginning of operation in JT-60SA,

a predictor with a line integrated density signal and the mode lock signal as privileged

information has been developed and tested with 1437 JET discharges. The success rate with

positive warning time has been improved from 45.24% to 90.48% and the tardy detection rate

has diminished from 50% to 8.33%. The use of privileged information in an adaptive way also

provides a remarkable reduction of false alarms from 11.53% to 1.15%. The potential of the

methodology, exemplified with data relevant to the beginning of JT-60SA operation, is

absolutely general and can be applied to any combination of diagnostic signals.