Latest developments in data analysis tools for disruption prediction and for the exploration of multimachine operational spaces.

López Navarro, Juan Manuel and Murari, A. and Vega, J. and Boutot, P. and Cannas, B. and Dormido-Canto, S. and Fanni, A. and Moreno, R. and Pau, A. and Sias, G. and Ramírez, J.M. and Verdoolaege, G. (2012). Latest developments in data analysis tools for disruption prediction and for the exploration of multimachine operational spaces.. In: "24th IAEA Fusion Energy Conference (FEC 2012). San Diego, USA. 8-13 October 2012", 08/10/2012 - 13/10/2012, San Diego, CA. pp. 1-8.

Description

Title: Latest developments in data analysis tools for disruption prediction and for the exploration of multimachine operational spaces.
Author/s:
  • López Navarro, Juan Manuel
  • Murari, A.
  • Vega, J.
  • Boutot, P.
  • Cannas, B.
  • Dormido-Canto, S.
  • Fanni, A.
  • Moreno, R.
  • Pau, A.
  • Sias, G.
  • Ramírez, J.M.
  • Verdoolaege, G.
Item Type: Presentation at Congress or Conference (Article)
Event Title: 24th IAEA Fusion Energy Conference (FEC 2012). San Diego, USA. 8-13 October 2012
Event Dates: 08/10/2012 - 13/10/2012
Event Location: San Diego, CA
Title of Book: Proceedings of the 24th IAEA Fusion Energy Conference
Date: 2012
Volume: IAEA-C
Subjects:
Faculty: E.U.I.T. Telecomunicación (UPM)
Department: Sistemas Electrónicos y de Control [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In the last years significant efforts have been devoted to the development of advanced data analysis tools to both predict the occurrence of disruptions and to investigate the operational spaces of devices, with the long term goal of advancing the understanding of the physics of these events and to prepare for ITER. On JET the latest generation of the disruption predictor called APODIS has been deployed in the real time network during the last campaigns with the new metallic wall. Even if it was trained only with discharges with the carbon wall, it has reached very good performance, with both missed alarms and false alarms in the order of a few percent (and strategies to improve the performance have already been identified). Since for the optimisation of the mitigation measures, predicting also the type of disruption is considered to be also very important, a new clustering method, based on the geodesic distance on a probabilistic manifold, has been developed. This technique allows automatic classification of an incoming disruption with a success rate of better than 85%. Various other manifold learning tools, particularly Principal Component Analysis and Self Organised Maps, are also producing very interesting results in the comparative analysis of JET and ASDEX Upgrade (AUG) operational spaces, on the route to developing predictors capable of extrapolating from one device to another.

More information

Item ID: 20248
DC Identifier: http://oa.upm.es/20248/
OAI Identifier: oai:oa.upm.es:20248
Official URL: http://fec2012.com/
Deposited by: Memoria Investigacion
Deposited on: 28 Mar 2014 16:43
Last Modified: 21 Apr 2016 23:00
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