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A machine learning approach based on generative topographic mapping for disruption prevention and avoidance at JET
Ecole Polytech Fed Lausanne, SPC, CH-1015 Lausanne, Switzerland.;Univ Cagliari, Elect & Elect Engn Dept, Piazza DArmi, I-09123 Cagliari, Italy.;Univ Cagliari, Dept Elect & Elect Engn, Piazza Armi 09123, Cagliari, Italy..
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Fusion Plasma Physics.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Fusion Plasma Physics.
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Fusion Plasma Physics.
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Number of Authors: 12292019 (English)In: Nuclear Fusion, ISSN 0029-5515, E-ISSN 1741-4326, Vol. 59, no 10, article id 106017Article in journal (Refereed) Published
Abstract [en]

The need for predictive capabilities greater than 95% with very limited false alarms are demanding requirements for reliable disruption prediction systems in tokamaks such as JET or, in the near future, ITER. The prediction of an upcoming disruption must be provided sufficiently in advance in order to apply effective disruption avoidance or mitigation actions to prevent the machine from being damaged. In this paper, following the typical machine learning workflow, a generative topographic mapping (GTM) of the operational space of JET has been built using a set of disrupted and regularly terminated discharges. In order to build the predictive model, a suitable set of dimensionless, machine-independent, physics-based features have been synthesized, which make use of 1D plasma profile information, rather than simple zero-D time series. The use of such predicting features, together with the power of the GTM in fitting the model to the data, obtains, in an unsupervised way, a 2D map of the multi-dimensional parameter space of JET, where it is possible to identify a boundary separating the region free from disruption from the disruption region. In addition to helping in operational boundaries studies, the GTM map can also be used for disruption prediction exploiting the potential of the developed GTM toolbox to monitor the discharge dynamics. Following the trajectory of a discharge on the map throughout the different regions, an alarm is triggered depending on the disruption risk of these regions. The proposed approach to predict disruptions has been evaluated on a training and an independent test set and achieves very good performance with only one tardive detection and a limited number of false detections. The warning times are suitable for avoidance purposes and, more important, the detections are consistent with physical causes and mechanisms that destabilize the plasma leading to disruptions.

Place, publisher, year, edition, pages
Institute of Physics Publishing (IOPP), 2019. Vol. 59, no 10, article id 106017
Keywords [en]
disruption prevention and avoidance, machine learning, artificial intelligence, dimensionless physics-based indicators, unsupervised learning and clustering of high-dimensional spaces, disruption classification and causes, JET tokamak real-time opearation and control
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-269146DOI: 10.1088/1741-4326/ab2ea9ISI: 000482571700001OAI: oai:DiVA.org:kth-269146DiVA, id: diva2:1414108
Note

QC 20200312

Available from: 2020-03-12 Created: 2020-03-12 Last updated: 2020-03-12Bibliographically approved

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Bergsåker, HenrikBykov, IgorFrassinetti, LorenzoGarcia Carrasco, AlvaroHellsten, TorbjörnJohnson, ThomasRachlew, ElisabethRatynskaia, SvetlanaRubel, MarekStefániková, EsteraStröm, PetterTholerus, EmmiTolias, PanagiotisOlivares, Pablo VallejosWeckmann, Armin
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