Adaptive Anomaly Detection Disruption Prediction Starting from First Discharge on Tokamak

Abstract

Plasma disruption presents a significant challenge in tokamak fusion, where it can cause severe damage and economic losses. Current disruption predictors mainly rely on data-driven methods, requiring extensive discharge data for training. However, future tokamaks require disruption prediction from the first shot, posing challenges of data scarcity during the early operation period. In this period disruption prediction aims to support safe exploration of operation range and accumulate necessary data to develop advanced prediction models. Thus, predictors must adapt to evolving plasma environments during this exploration phase. To address these issues, this study proposes a cross-tokamak adaptive deployment method using the Enhanced Convolutional Autoencoder Anomaly Detection (E-CAAD) predictor, enabling disruption prediction from the first shot of new devices. Experimental results indicate the ability of E-CAAD model trained on existing devices to effectively differentiate between disruption precursors and non-disruption samples on new devices, proving the feasibility of model cross-device transfer. Building upon this, adaptive learning from scratch and threshold adaptive adjustment strategies are proposed to achieve model cross-device transfer. The adaptive learning from scratch strategy enables the predictor to use scarce data during the early operation of the new device while rapidly adapting to changes in operation environment. The threshold adaptive adjustment strategy addresses the challenge of selecting warning thresholds on new devices where validation set is lacking, ensuring that the warning thresholds adapt to changes in the operation environment. Finally, experiments transferring the model from J-TEXT to EAST exhibit comparable performance to EAST models trained with ample data, achieving a TPR of 85.88% and a FPR of 6.15%, with a 20ms reserved MGI system reaction time.