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An adaptive real-time disruption predictor for ASDEX Upgrade

机译:用于ASDEX升级的自适应实时中断预测器

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摘要

In this paper, a neural predictor has been built using plasma discharges selected from two years of ASDEX Upgrade experiments, from July 2002 to July 2004. In order to test the real-time prediction capability of the system, its performance has been evaluated using discharges coming from different experimental campaigns, from June 2005 to July 2007. All disruptions that occurred in the chosen experimental campaigns were included with the exception of those occurring in the ramp-up phase, in the ramp-down phase (if the disruption does not happen in the first 100 ms), those caused by massive gas injection and disruptions following vertical displacement events. The large majority of selected disruptions are of the cooling edge type and typically preceded by the growth of tearing modes, degradation of the thermal confinement and enhanced plasma radiation. A very small percentage of them happen at large beta after a short precursor phase. For each discharge, seven plasma diagnostic signals have been selected from numerous signals available in real-time. During the training procedure, a self-organizing map has been used to reduce the database size in order to improve the training of the neural network. Moreover, an optimization procedure has been performed to discriminate between safe and pre-disruptive phases. The prediction success rate has been further improved, performing an adaptive training of the network whenever a missed alarm is triggered by the predictor.
机译:在本文中,从2002年7月至2004年7月的两年ASDEX升级实验中选择了等离子体放电来构建神经预测器。为了测试系统的实时预测能力,已使用放电评估了其性能。来自2005年6月至2007年7月的不同实验活动。选定的实验活动中发生的所有中断都包括在内,但在加速阶段,下降阶段发生的中断除外(如果未发生中断)在最初的100毫秒内),是由于大量气体注入和垂直位移事件后的破坏所引起的。大部分选定的破坏是冷却边缘类型的,通常发生在撕裂模式的增长,热限制的降低和增强的等离子体辐射之前。在极短的前驱阶段之后,它们中的很小一部分发生在较大的beta位置。对于每次放电,已经从众多实时可用信号中选择了七个等离子体诊断信号。在训练过程中,自组织映射已用于减少数据库大小,以改善神经网络的训练。此外,已经执行了优化程序以区分安全阶段和破坏前阶段。预测成功率得到进一步提高,每当预测器触发错过的警报时,都对网络进行自适应训练。

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