Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees
- 7 February 2008
- journal article
- Published by IOP Publishing in Nuclear Fusion
Abstract
Disruptions remain one of the most hazardous events in the operation of a tokamak device, since they can cause damage to the vacuum vessel and surrounding structures. Their potential danger increases with the plasma volume and energy content and therefore they will constitute an even more serious issue for the next generation of machines. For these reasons, in the recent years a lot of attention has been devoted to devise predictors, capable of foreseeing the imminence of a disruption sufficiently in advance, to allow time for undertaking remedial actions. In this paper, the results of applying fuzzy logic and classification and regression trees (CART) to the problem of predicting disruptions at JET are reported. The conceptual tools of fuzzy logic, in addition to being well suited to accommodate the opinion of experts even if not formulated in mathematical but linguistic terms, are also fully transparent, since their governing rules are human defined. They can therefore help not only in forecasting disruptions but also in studying their behaviour. The analysis leading to the rules of the fuzzy predictor has been complemented with a systematic investigation of the correlation between the various experimental signals and the imminence of a disruption. This has been performed with an exhaustive, non-linear and unbiased method based on decision trees. This investigation has confirmed that the relative importance of various signals can change significantly depending on the plasma conditions. On the basis of the results provided by CART on the information content of the various quantities, the prototype of an adaptive fuzzy logic predictor was trained and tested on JET database. Its performance is significantly better than the previous static one, proving that more flexible prediction strategies, not uniform over the whole discharge but tuned to the operational region of the plasma at any given time, can be very competitive and should be investigated systematically.Keywords
This publication has 13 references indexed in Scilit:
- Disruption forecasting at JET using neural networksNuclear Fusion, 2003
- Neural-net disruption predictor in JT-60UNuclear Fusion, 2003
- Fuzzy time series approach for disruption prediction in tokamak reactorsIEEE Transactions on Magnetics, 2003
- On-line prediction and mitigation of disruptions in ASDEX UpgradeNuclear Fusion, 2002
- Forecasting disruptions in the ADITYA tokamak using neural networksNuclear Fusion, 2000
- Forecast of TEXT plasma disruptions using soft X rays as input signal in a neural networkNuclear Fusion, 1999
- Tokamak disruption alarm based on a neural network model of the high- beta limitNuclear Fusion, 1997
- Neural network prediction of some classes of tokamak disruptionsNuclear Fusion, 1996
- Disruptions in tokamaksPlasma Physics and Controlled Fusion, 1995
- Disruptions in JETNuclear Fusion, 1989