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Development of an efficient real-time disruption predictor from scratch on JET and implications for ITER (Articolo in rivista)

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Development of an efficient real-time disruption predictor from scratch on JET and implications for ITER (Articolo in rivista) (literal)

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S. Dormido-Canto; J. Vega; J.M. Ramírez; A. Murari; R. Moreno; J.M. López; A. Pereira; JET-EFDA Contributors (2013)
Development of an efficient real-time disruption predictor from scratch on JET and implications for ITER
in Nuclear fusion; International Atomic Energy Agency, Vienna (Austria); IOP PUBLISHING LTD, BRISTOL BS1 6BE (Regno Unito)
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S. Dormido-Canto; J. Vega; J.M. Ramírez; A. Murari; R. Moreno; J.M. López; A. Pereira; JET-EFDA Contributors (literal)

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JET-EFDA Culham Science Centre, Abingdon, OX14 3DB, UK / 1,3 : Dpto. Informática y Automática-UNED, Madrid, Spain / 2,5,7 : Asociación EURATOM/CIEMAT para Fusión, Madrid, Spain / 4 : Associazione EURATOM/ENEA per la Fusione, Consorzio RFX, 4-35127 Padova, Italy / 6 Grupo de Investigación en Intrumentación y Acústica Aplicada, UPM, Madrid, Spain / 8 : See the appendix of Romanelli F. et al 2012 Proc. 24th IAEA Fusion Energy Conf. (San Diego, CA, 2012). (literal)

Titolo

Development of an efficient real-time disruption predictor from scratch on JET and implications for ITER (literal)

Abstract

Prediction of disruptions from scratch is an ITER-relevant topic. The first operations with the new ITER-like wall constitute a good opportunity to test the development of new predictors from scratch and the related methodologies. These methodologies have been based on the Advanced Predictor Of DISruptions (APODIS) architecture. APODIS is a real-time disruption predictor that is in operation in the JET real-time network. Balanced and unbalanced datasets are used to develop real-time predictors from scratch. The discharges are used in chronological order. Also, different criteria to decide when to re-train a predictor are discussed. The best results are obtained by applying a hybrid method (balanced/unbalanced datasets) for training and with the criterion of re-training after every missed alarm. The predictors are tested off-line with all the discharges (disruptive/non-disruptive) corresponding to the first three JET ITER-like wall campaigns. The results give a success rate of 93.8% and a false alarm rate of 2.8%. It should be considered that these results are obtained from models trained with no more than 42 disruptive discharges. (literal)

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