Real-Time Implementation in JET of the SPAD Disruption Predictor Using MARTe

Esquembri, S.; Vega, J.; Murari, A.; Ruiz, M.; Barrera, E.; Dormido-Canto, S.; Felton, R.; Tsalas, M.; Valcárcel, D.

doi:10.1109/tns.2018.2791719

Cited by 16 publications

(17 citation statements)

References 20 publications

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“…SPAD has a higher disruption detection success rate and earlier detection time. SPAD is being installed in the JET real-time network [107]. The ongoing effort for improvement focuses particularly in the direction of reducing the dispersion of the warning time, at present too large to allow detailed adjustments of the mitigation strategies.…”

Section: Disruption Predictionmentioning

confidence: 99%

Overview of the JET results in support to ITER

Litaudon¹,

Abduallev²,

Abhangi³

et al. 2017

Nucl. Fusion

160

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The 2014–2016 JET results are reviewed in the light of their significance for optimising the ITER research plan for the active and non-active operation. More than 60 h of plasma operation with ITER first wall materials successfully took place since its installation in 2011. New multi-machine scaling of the type I-ELM divertor energy flux density to ITER is supported by first principle modelling. ITER relevant disruption experiments and first principle modelling are reported with a set of three disruption mitigation valves mimicking the ITER setup. Insights of the L–H power threshold in Deuterium and Hydrogen are given, stressing the importance of the magnetic configurations and the recent measurements of fine-scale structures in the edge radial electric. Dimensionless scans of the core and pedestal confinement provide new information to elucidate the importance of the first wall material on the fusion performance. H-mode plasmas at ITER triangularity (H = 1 at βN ~ 1.8 and n/nGW ~ 0.6) have been sustained at 2 MA during 5 s. The ITER neutronics codes have been validated on high performance experiments. Prospects for the coming D–T campaign and 14 MeV neutron calibration strategy are reviewed.

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Section: Disruption Predictionmentioning

confidence: 99%

Overview of the JET results in support to ITER

Litaudon¹,

Abduallev²,

Abhangi³

et al. 2017

Nucl. Fusion

160

View full text Add to dashboard Cite

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“…Therefore, the idea is to use only the ML signal but after mapping it into a two-dimensional feature space. This mapping is an evolution of a recent approach focused on anomaly detection to recognise disruptive events [29][30][31].…”

Section: Application To Jet Of the Centroid Methods For Mitigation Pu...mentioning

confidence: 99%

A linear equation based on signal increments to predict disruptive behaviours and the time to disruption on JET

et al. 2019

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This article describes the development of a generic disruption predictor that is also used as basic system to provide an estimation of the time to disruption at the alarm times. The mode lock signal normalised to the plasma current is used as input feature. The recognition of disruptive/non-disruptive behaviours is not based on a simple threshold of this quantity but on the evolution of the amplitudes between consecutive samples taken periodically. The separation frontier between plasma behaviours (disruptive/non-disruptive) is linear in such parameter space. The percentages of recognised and false alarms are 98% and 4%, respectively. The recognised alarms can be split into valid alarms (90%) and late detections (8%). The experimental distribution of warning times follows an exponential model with average warning time of 443 ms. On the other hand, the prediction of the time to the disruption has been fitted to a Weibull model that relates this predicted time to the distance of the points to the diagonal in the parameter space of consecutive samples. The model shows a very good agreement between predicted times and warning times in narrow time intervals (between 0.01 s and 0.06 s) before the disruption.

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“…Of the three machine-learning-based predictors that were implemented in JET's real-time network, APODIS 17 , SPAD 36 and Centroid 37 , the former correctly identified disruptions in more than 98% of cases and had a false alarm rate (that is, wrongly classified non-disruptive data) in fewer than 2% of cases, with an average warning time of hundreds of milliseconds.…”

Section: And Jet Contributors*mentioning

confidence: 99%