Cross-Tokamak Deployment Study of Plasma Disruption Predictors Based on Convolutional Autoencoder
arxiv(2023)
摘要
In the initial stages of operation for future tokamak, facing limited data
availability, deploying data-driven disruption predictors requires optimal
performance with minimal use of new device data. This paper studies the issue
of data utilization in data-driven disruption predictor during cross tokamak
deployment. Current predictors primarily employ supervised learning methods and
require a large number of disruption and non-disruption shots for training.
However, the scarcity and high cost of obtaining disruption shots for future
tokamaks result in imbalanced training datasets, reducing the performance of
supervised learning predictors. To solve this problem, we propose the Enhanced
Convolutional Autoencoder Anomaly Detection (E-CAAD) predictor. E-CAAD can be
only trained by normal samples from non-disruption shots and can also be
trained by disruption precursor samples when disruption shots occur. This model
not only overcomes the sample imbalance in supervised learning predictors, but
also overcomes the inefficient dataset utilization faced by traditional anomaly
detection predictors that cannot use disruption precursor samples for training,
making it more suitable for the unpredictable datasets of future tokamaks.
Compared to traditional anomaly detection predictor, the E-CAAD predictor
performs better in disruption prediction and is deployed faster on new devices.
Additionally, we explore strategies to accelerate deployment of E-CAAD
predictor on the new device by using data from existing devices. Two deployment
strategies are presented: mixing data from existing devices and fine-tuning the
predictor trained on existing devices. Our comparisons indicate that the data
from existing device can accelerate the deployment of predictor on new device.
Notably, the fine-tuning strategy yields the fastest deployment on new device
among the designed strategies.
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