Adaptive Anomaly Detection Disruption Prediction Starting from First Discharge on Tokamak
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 t...
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Main Authors | , , , , , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
12.04.2024
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2404.08241 |