Multi-mode remote participation on the GOLEM tokamak

Data-driven technique for disruption prediction in GOLEM tokamak using stacked ensembles with active learning

In a tokamak, disruption is defined as losing control over a confined plasma resulting in sudden extinction of the plasma current. Machine learning offers potent solutions to classify plasma discharges into disruptive and non-disruptive classes. Evolving experimental programs reduce the performance of machine learning models, and also, the need for labeling the huge volume of data incurs more labor cost and time. This paper proposes a data-driven based machine learning technique that employs an active learning approach for labeling and classification of plasma discharges. The designed model uses 117 normally terminated shots and 70 disruptive shots with 14 labeled diagnostic signals. The stacking classifier is built over three base learners: logistic regression, reduced error pruning tree, and categorial boost algorithm, and the logistic regression technique is used at the meta-learner. An active learning approach is proposed for labeling the unlabeled dataset using a modified uncertainty sampling technique with minimal queries. The proposed model queries the unlabeled data to an oracle based on a selection strategy with uncertainty sampling using entropy metrics. The new labeled data and the class probabilities of the base classifiers are channeled to the final predictor for classifying the plasma discharge. The proposed model achieves an accuracy of 98.75% in classifying the disruptive vs non-disruptive discharges, with a minimally trained dataset, and also, it is free from aging of predictors.

Data-driven disruption prediction in GOLEM Tokamak using ensemble classifiers

A robust disruption prediction system is mandatory in a Tokamak control system as the disruption can cause malfunctioning of the plasma-facing components and impair irrecoverable structural damage to the vessel. To mitigate the disruption, in this article, a data-driven based disruption predictor is developed using an ensemble technique. The ensemble algorithm classifies disruptive and non-disruptive discharges in the GOLEM Tokamak system. Ensemble classifiers combine the predictive capacity of several weak learners to produce a single predictive model and are utilized both in supervised and unsupervised learning. The resulting final model reduces the bias, minimizes variance and is unlikely to over-fit when compared to the individual model from a single algorithm. In this paper, popular ensemble techniques such as Bagging, Boosting, Voting, and Stacking are employed on the time-series Tokamak dataset, which consists of 117 normal and 70 disruptive shots. Stacking ensemble with REPTree (Reduced Error Pruning Tree) as a base learner and Multi-response Linear Regression as meta learner produced better results in comparison to other ensembles. A comparison with the widely employed stand-alone machine learning algorithms and ensemble algorithms are illustrated. The results show the excellent performance of the Stacking model with an F1 score of 0.973. The developed predictive model would be capable of warning the human operator with feedback about the feature(s) causing the disruption.

Low cost alternative of high speed visible light camera for tokamak experiments

We present design, analysis, and performance evaluation of a new, low cost and high speed visible-light camera diagnostic system for tokamak experiments. The system is based on the camera Casio EX-F1, with the overall price of approximately a thousand USD. The achieved temporal resolution is up to 40 kHz. This new diagnostic was successfully implemented and tested at the university tokamak GOLEM (R = 0.4 m, a = 0.085 m, B(T) < 0.5 T, I(p) < 4 kA). One possible application of this new diagnostic at GOLEM is discussed in detail. This application is tomographic reconstruction for estimation of plasma position and emissivity.