Disruption prediction at ASDEX Upgrade using neural networks

Barbara Cannas, Alessandra Fanni, Gabriella Pautasso, Giuliana Sias, Piergiorgio Sonato, Maria Katiuscia Zedda

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

In this paper, a Multi Layer Perceptron is trained to act as disruptions predictor at ASDEX Upgrade. In particular, an optimization procedure is performed to identify a time instant that discriminate between disruptive and safe phases of disruptive discharges. The neural predictor has been trained, validated and tested using 149 disruptive pulses, selected from two years of ASDEX Upgrade experiments from 2002 to 2004. Non disruptive pulses has not been used to design the predictor, because the disruptive discharges at ASDEX Upgrade present a safe phase sufficiently long to well represent also the behavior of safe pulses. In order to limit the neural network size, for each disruptive shot, seven plasma diagnostic signals have been selected from numerous signals available in real time. A Self Organizing Map has been used to reduce the shot dimensionality in order to improve the training of the Multi Layer Perceptron, greatly increasing the prediction capability of the system. The results are quite good, with a prediction success rate greater than 90%.
Original languageEnglish
Publication statusPublished - 2006
Externally publishedYes
Event33rd European Physical Society Conference on Plasma Physics 2006, EPS 2006 - , Italy
Duration: 1 Jan 2006 → …

Conference

Conference33rd European Physical Society Conference on Plasma Physics 2006, EPS 2006
CountryItaly
Period1/1/06 → …

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All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics

Cite this

Cannas, B., Fanni, A., Pautasso, G., Sias, G., Sonato, P., & Zedda, M. K. (2006). Disruption prediction at ASDEX Upgrade using neural networks. Paper presented at 33rd European Physical Society Conference on Plasma Physics 2006, EPS 2006, Italy.