Real-time disruption prediction in the plasma control system of HL-2A based on deep learning

Abstract

A real-time disruption predictor based on deep learning method is implemented into the Plasma Control System (PCS) of HL-2A. It has a total accuracy of 89.0% during the online testing of Shot Nos. 38,650–39,347 in HL-2A. 32 false alarms are triggered during the 142 non-disruptive shots and 10 disruptions are missed in the 240 disruptive shots. In several shots, this disruption prediction algorithm is used to trigger the disruption mitigation system, namely, massive gas injection (MGI) and supersonic molecular beam injection (SMBI) in this experiment. For MGI, there is a 12-millisecond delay between the trigger signal and the mitigated disruption. In the 240 disruptive shots, 81.5% of them are predicted with more than 12 ms in advance and thus can be successfully mitigated. For SMBI, the delay is 4 millisecond and 91.3% of the disruptions can be successfully mitigated. In general, although dedicated work is needed to close the gap between online disruption prediction and the offline algorithms, the effectiveness of deep learning-based disruption predictors in real-time environments is validated in this research.