Global optimization driven by genetic algorithms for disruption predictors based on APODIS architecture

• Since year 2010, the APODIS architecture has proven its accuracy predicting disruptions in JET tokamak. Nevertheless, it has shown margins for improvements, fact indisputable after the enhanced performances achieved in posterior upgrades. In this article, a complete optimization driven by Genetic Algorithms (GA) is applied to it aiming at considering all possible combination of signals, signal features, quantity of models, their characteristics and internal parameters. This global optimization targets the creation of the best possible system with a reduced amount of required training data. The results harbor no doubts about the reliability of the global optimization method, allowing to outperform the ones of previous versions: 91.77% of predictions (89.24% with an anticipation higher than 10 ms) with a 3.55% of false alarms. Beyond its effectiveness, it also provides the potential opportunity to develop a spectrum of future predictors using different training datasets. (C) 2016 Elsevier B.V. All rights reserved.

Subject headings

NATURVETENSKAP  -- Fysik -- Fusion, plasma och rymdfysik (hsv//swe)

NATURAL SCIENCES  -- Physical Sciences -- Fusion, Plasma and Space Physics (hsv//eng)

NATURVETENSKAP  -- Fysik -- Subatomär fysik (hsv//swe)

NATURAL SCIENCES  -- Physical Sciences -- Subatomic Physics (hsv//eng)

Keyword

Disruption prediction

Genetic Algorithms

JET

APODIS

ITER

Publication and Content Type

ref (subject category)

art (subject category)