Improvements in disruption prediction at ASDEX Upgrade

• Published in: Fusion engineering and design 2015-10, Vol.96-97, p.698-702
• Main Authors: Aledda, R., Cannas, B., Fanni, A., Pau, A., Sias, G.
• Format: Article
• Language: English
• Subjects: Assessments; Availability; Design engineering; Disruption; Disruption prediction; Logistic regressor; Logistics; Machine learning; Mahalanobis distance; Nuclear fusion; Temporal logic; Upgrading
• ISSN: 0920-3796; 1873-7196
• DOI: 10.1016/j.fusengdes.2015.03.045

•A disruption prediction system for AUG, based on a logistic model, is designed.•The length of the disruptive phase is set for each disruption in the training set.•The model is tested on dataset different from that used during the training phase.•The generalization capability and the aging of the model have been tested.•The predictor performance is compared with the locked mode detector. In large-scale tokamaks disruptions have the potential to create serious damage to the facility. Hence disruptions must be avoided, but, when a disruption is unavoidable, minimizing its severity is mandatory. A reliable detection of a disruptive event is required to trigger proper mitigation actions. To this purpose machine learning methods have been widely studied to design disruption prediction systems at ASDEX Upgrade. The training phase of the proposed approaches is based on the availability of disrupted and non-disrupted discharges. In literature disruptive configurations were assumed appearing into the last 45ms of each disruption. Even if the achieved results in terms of correct predictions were good, it has to be highlighted that the choice of such a fixed temporal window might have limited the prediction performance. In fact, it generates confusing information in cases of disruptions with disruptive phase different from 45ms. The assessment of a specific disruptive phase for each disruptive discharge represents a relevant issue in understanding the disruptive events. In this paper, the Mahalanobis distance is applied to define a specific disruptive phase for each disruption, and a logistic regressor has been trained as disruption predictor. The results show that enhancements on the achieved performance on disruption prediction are possible by defining a specific disruptive phase for each disruption.