Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection

The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-driven disruption predictors employ supervised learning techniques, which require labeled training data. The manual labeling of disruption precursors is a tedious and challenging task, as some precursors are difficult to accurately identify, limiting the potential of machine learning models. To address this issue, commonly used labeling methods assume that the precursor onset occurs at a fixed time before the disruption, which may not be consistent for different types of disruptions or even the same type of disruption, due to the different speeds at which plasma instabilities escalate. This leads to mislabeled samples and suboptimal performance of the supervised learning predictor. In this paper, we present a disruption prediction method based on anomaly detection that overcomes the drawbacks of unbalanced positive and negative data samples and inaccurately labeled disruption precursor samples. We demonstrate the effectiveness and reliability of anomaly detection predictors based on different algorithms on J-TEXT and EAST to evaluate the reliability of the precursor onset time inferred by the anomaly detection predictor. The precursor onset times inferred by these predictors reveal that the labeling methods have room for improvement as the onset times of different shots are not necessarily the same. Finally, we optimize precursor labeling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.Comment: 21 pages, 11 figure

Enhancement of EAST plasma control capabilities

In order to improve the plasma control performance and enhance the capability for advanced plasma control, new algorithms such as PEFIT/ISOFLUX plasma shape feedback control, quasi-snowflake plasma shape development and vertical control under new vertical control power supply, have been implemented and experimentally tested and verified in EAST 2014 campaign. P-EFIT is a rewritten version of EFIT aiming at fast real-time equilibrium reconstruction by using GPU for parallelized computation. Successful control using PEFIT/ISOFLUX was established in dedicated experiment. Snowfldivertor plasma shape has the advantage of spreading heat over the divertor target and a quasi-snowflake (QSF) configuration was achieved in discharges with Ip =0.25 MA and Bt =1.8T, κ∼1.9, by plasma position feedback control. The shape feedback control to achieve QSF shape has been preliminary implemented by using PEFIT and the initial experimental test has been done. For more robust vertical instability control, the inner coil (IC) and its power supply have been upgraded. A new control algorithm with the combination of Bang-bang and PID controllers has been developed. It is shown that new vertical control power supply together with the new control algorithms results in higher vertical controllability