Disruption avoidance through the prevention of NTM destabilization in TCV

Sheikh, U.; Duval, B.P.; Galperti, C.; Maraschek, M.; Sauter, O.; Sozzi, C.; Granucci, G.; Kong, M.; Labit, B.; Merle, A.; Rispoli, N.

doi:10.1088/1741-4326/aad924

Cited by 13 publications

(12 citation statements)

References 29 publications

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“…At time point , the 2/1 NTM disappears while an n = 2 locked mode appears. Between 1.6 s and 2 s, the mode consecutively unlocks and locks [44], while the plasma position is oscillating. At time point the plasma disrupts.…”

Section: Experimental Results Of Integrated Kinetic Profile and Ntm C...mentioning

confidence: 99%

“…In this discharge, a 2/1 NTM accelerates to twice its initial frequency. Subsequently it disappears while a n = 2 locked mode appears at 1.6 s. Although further analysis in [44] reveals that the mode spins up and locks consecutively between 1.6 s and 2 s, these events happen too fast for the real-time MHD analysis to pick up. These fast transients can be seen in the magnetics spectogram.…”

Section: Real-time Detection Of Ntms and Locked Modes On Tcvmentioning

confidence: 95%

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Real-time plasma state monitoring and supervisory control on TCV

et al. 2019

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In ITER and DEMO, various control objectives related to plasma control must be simultaneously achieved by the plasma control system (PCS), in both normal operation as well as off-normal conditions. The PCS must act on off-normal events and deviations from the target scenario, since certain sequences (chains) of events can precede disruptions. It is important that these decisions are made while maintaining a coherent prioritization between the real-time control tasks to ensure high-performance operation. In this paper, a generic architecture for task-based integrated plasma control is proposed. The architecture is characterized by the separation of state estimation, event detection, decisions and task execution among different algorithms, with standardized signal interfaces. Central to the architecture are a plasma state monitor and supervisory controller. In the plasma state monitor, discrete events in the continuous-valued plasma state are modeled using finite state machines. This provides a high-level representation of the plasma state. The supervisory controller coordinates the execution of multiple plasma control tasks by assigning task priorities, based on the finite states of the plasma and the pulse schedule. These algorithms were implemented on the TCV digital control system and integrated with actuator resource management and existing state estimation algorithms and controllers. The plasma state monitor on TCV can track a multitude of plasma events, related to plasma current, rotating and locked neoclassical tearing modes, and position displacements. In TCV experiments on simultaneous control of plasma pressure, safety factor profile and NTMs using electron cyclotron heating (ECH) and current drive (ECCD), the supervisory controller assigns priorities to the relevant control tasks. The tasks are then executed by feedback controllers and actuator allocation management. This work forms a significant step forward in the ongoing integration of control capabilities in experiments on TCV, in support of tokamak reactor operation.

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Section: Experimental Results Of Integrated Kinetic Profile and Ntm C...mentioning

confidence: 99%

Section: Real-time Detection Of Ntms and Locked Modes On Tcvmentioning

confidence: 95%

Real-time plasma state monitoring and supervisory control on TCV

et al. 2019

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“…The combination of machine learning tools, with new interesting strategies for prevention and avoidance [26], such as the application of rotating magnetic perturbations, looks very promising. The stabilization of NTM modes, with feedback control of electron cyclotron deposition near the q = 2 surface, has been demonstrated to prevent disruptions in TCV; supported by machine learning tools for prediction, it could be a viable solution for ITER [27,28]. With suitable guidance from physical considerations, data-driven techniques can also help modelling the stability boundaries [29].…”

Section: The Importance Of Disruption Classification For Predictionmentioning

confidence: 99%

Stacking of predictors for the automatic classification of disruption types to optimize the control logic

et al. 2021

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Nowadays, disruption predictors, based on machine learning techniques, can perform well but they typically do not provide any information about the type of disruption and cannot predict the time remaining before the current quench. On the other hand, the automatic identification of the disruption type is a crucial aspect required to optimize the remedial actions and a prerequisite to forecasting the time left for intervening. In this work, a stack of machine learning tools is applied to the task of automatic classification of the disruption types. The strategy is implemented from scratch and completely adaptive; the predictors start operating after the first disruption and update their own models, following the evolution of the experimental program, without any human intervention. Moreover, they are designed to implement a form of transfer learning, in the sense that they identify autonomously the most important disruption classes, generating new ones when necessary. The results obtained are very encouraging in terms of both prediction performance and classification accuracy. On the other hand, regarding the narrowing of the warning times, some progress has been achieved, but new techniques will have to be devised to obtain fully satisfactory properties.

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“…In the last couple of years, therefore, many efforts have been devoted to the developments of parsimonious data-driven techniques that can provide a good success rate of prediction after a few tens of disruptions and even after the first disruption [19][20][21]. Their future application is very promising, particularly if coupled with new methods to avoid disruptions [22][23][24] and to model the stability boundary [25].…”

Section: Disruptions In Tokamaks: An Operational Perspectivementioning

confidence: 99%

Investigating the Physics of Tokamak Global Stability with Interpretable Machine Learning Tools

et al. 2020

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The inadequacies of basic physics models for disruption prediction have induced the community to increasingly rely on data mining tools. In the last decade, it has been shown how machine learning predictors can achieve a much better performance than those obtained with manually identified thresholds or empirical descriptions of the plasma stability limits. The main criticisms of these techniques focus therefore on two different but interrelated issues: poor “physics fidelity” and limited interpretability. Insufficient “physics fidelity” refers to the fact that the mathematical models of most data mining tools do not reflect the physics of the underlying phenomena. Moreover, they implement a black box approach to learning, which results in very poor interpretability of their outputs. To overcome or at least mitigate these limitations, a general methodology has been devised and tested, with the objective of combining the predictive capability of machine learning tools with the expression of the operational boundary in terms of traditional equations more suited to understanding the underlying physics. The proposed approach relies on the application of machine learning classifiers (such as Support Vector Machines or Classification Trees) and Symbolic Regression via Genetic Programming directly to experimental databases. The results are very encouraging. The obtained equations of the boundary between the safe and disruptive regions of the operational space present almost the same performance as the machine learning classifiers, based on completely independent learning techniques. Moreover, these models possess significantly better predictive power than traditional representations, such as the Hugill or the beta limit. More importantly, they are realistic and intuitive mathematical formulas, which are well suited to supporting theoretical understanding and to benchmarking empirical models. They can also be deployed easily and efficiently in real-time feedback systems.

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Disruption avoidance through the prevention of NTM destabilization in TCV

Cited by 13 publications

References 29 publications

Real-time plasma state monitoring and supervisory control on TCV

Real-time plasma state monitoring and supervisory control on TCV

Stacking of predictors for the automatic classification of disruption types to optimize the control logic

Investigating the Physics of Tokamak Global Stability with Interpretable Machine Learning Tools

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