A real-time disruption predictor using random forest was developed for high-density disruptions and used in the plasma control system (PCS) of the EAST tokamak for the first time. The disruption predictor via random forest (DPRF) ran in piggyback mode and was actively exploited in dedicated experiments during the 2019–2020 experimental campaign to test its real-time predictive capabilities in oncoming high-density disruptions. During dedicated experiments, the mitigation system was triggered by a presetalarm provided by DPRF and neon gas was injected into the plasma to successfully mitigate disruption damage. DPRF’s average computing time of ∼250 μs is also an extremely relevant result, considering that the algorithm provides not only the probability of an impending disruption, i.e. the disruptivity, but also the so-called feature contributions, i.e. explainability estimates to interpret in real time the drivers of the disruptivity. DPRF was trained with a dataset of disruptions in which the electron density reached at least 80% of the Greenwald density limit, using the zero-dimensional signal routinely available to the EAST PCS. Through offline analysis, an optimal warning threshold on the DPRF disruptivity signal was found, which allows for a successful alarm rate of 92% and a false alarm rate of 9.9%. By analyzing the false alarm causes, we find that a fraction (∼15%) of the misclassifications are due to sudden transitions of plasma confinement from H- to L-mode, which often occur during high-density discharges in EAST. By analyzing DPRF feature contributions, it emerges that the loop voltage signal is that main cause of such false alarms: plasma signals more apt to characterize the confinement back-transition should be included to avoid false alarms.

使用随机森林的实时中断预测器是为高密度中断开发的，并首次用于 EAST 托卡马克的等离子体控制系统 (PCS)。通过随机森林 (DPRF) 的中断预测器以搭载模式运行，并在 2019-2020 年实验活动期间的专用实验中得到积极利用，以测试其对即将到来的高密度中断的实时预测能力。在专门的实验中，缓解系统由 DPRF 提供的预设警报触发，并将氖气注入等离子体中，以成功缓解中断损伤。DPRF 的平均计算时间约为 250 μs 也是一个极其相关的结果，考虑到该算法不仅提供即将发生的破坏的概率，即破坏性，而且提供所谓的特征贡献，即实时解释破坏性驱动因素的可解释性估计。DPRF 使用电子密度达到格林沃尔德密度极限的至少 80% 的中断数据集进行训练，使用 EAST PCS 常规可用的零维信号。通过离线分析，找到了DPRF破坏性信号的最优告警阈值，成功告警率为92%，误报率为9.9%。通过分析误报原因​​，我们发现一小部分（~15%）的错误分类是由于等离子体限制从 H 模式到 L 模式的突然转变，这通常发生在 EAST 的高密度放电过程中。通过分析 DPRF 特征贡献，发现环路电压信号是此类误报的主要原因：应包括更易于表征限制反向转变的等离子体信号以避免误报。