A generic method for real time detection of magnetic sensor failure on tokamaks

Nouailletas, R.; Moreau, Philippe; Brémond, S.

doi:10.1016/j.fusengdes.2012.01.019

Cited by 10 publications

(19 citation statements)

References 10 publications

(14 reference statements)

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“…In fact, our proposed method takes less than 1 msec on a typical personal computer. The hyperparameters are prepared beforehand based on many previous discharges, and missing or faulty signals can be identified 6,7 in real-time. What one requires to do is simply to perform the following…”

Section: Based On Bayes' Model Coupled With Gaussian Processmentioning

confidence: 99%

“…Likelihood is constructed using Gauss's law for magnetism and Ampère's law and ensuring the consistency with the measured data. A couple of algorithms to detect faulty magnetic sensors in real time have been developed, 6,7 and an inference method for just one faulty signal has also been proposed. 7 Our proposed method in this work is tested with up to nine non-consecutive missing magnetic probe signals installed on KSTAR.…”

Section: Introductionmentioning

confidence: 99%

“…A couple of algorithms to detect faulty magnetic sensors in real time have been developed, 6,7 and an inference method for just one faulty signal has also been proposed. 7 Our proposed method in this work is tested with up to nine non-consecutive missing magnetic probe signals installed on KSTAR. 8 We find that the method a) Contributed paper published as part of the Proceedings of the 22nd Topical Conference on High-Temperature Plasma Diagnostics, San Diego, California, April, 2018. b) smjoung@kaist.ac.kr c) ycghim@kaist.ac.kr infers the correct values in less than 1 msec on a typical personal computer.…”

Section: Introductionmentioning

confidence: 99%

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Imputation of faulty magnetic sensors with coupled Bayesian and Gaussian processes to reconstruct the magnetic equilibrium in real time

Joung

Kim

Kwak

et al. 2018

Review of Scientific Instruments

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A Bayesian with GP(Gaussian Process)-based numerical method to impute a few missing magnetic signals caused by impaired magnetic probes during tokamak operations is developed such that the real-time reconstruction of magnetic equilibria, whose performance strongly depends on the measured magnetic signals and their intactness, are affected minimally. Likelihood of the Bayesian model constructed with the Maxwell's equations, specifically Gauss's law for magnetism and Ampère's law, results in infinite number of solutions if two or more magnetic signals are missing. This undesirable characteristic of the Bayesian model is remediated by coupling the model with the Gaussian process. Our proposed numerical method infers nine non-consecutive missing magnetic signals correctly in less than 1 msec suitable for real-time reconstruction of magnetic equilibria during tokamak operations.

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Section: Based On Bayes' Model Coupled With Gaussian Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Imputation of faulty magnetic sensors with coupled Bayesian and Gaussian processes to reconstruct the magnetic equilibrium in real time

Joung

Kim

Kwak

et al. 2018

Review of Scientific Instruments

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“…It should also be noticed that since some of the probes that are being validated are also inputs to the XLOC algorithm, as soon as a probe is declared as invalid, by a valid instance of XLOC, all the algorithm instances where the probe was being used as input can no longer be exploited until the end of the experiment. One way of overcoming this problem is to replace the faulty instance in the faulty XLOC by a synthetic probe, generated by a different XLOC, or to interpolate its value based on the values provided by neighbouring probes [12].…”

Section: Magnetics Fault Detectionmentioning

confidence: 99%

A real-time architecture for the identification of faulty magnetic sensors in the JET tokamak

Neto¹,

Alves²,

Carvalho³

et al. 2012

2012 18th IEEE-NPSS Real Time Conference

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In a tokamak, the accurate estimation of the plasma boundary is essential to maximise the fusion performance and is also the first line of defence for the physical integrity of the device. In particular, the first wall components might get severely damaged if over-exposed to a high plasma thermal load.The most common approach to calculate the plasma geometry and related parameters is based in a large set of different types of magnetic sensors. Using this information, real-time plasma equilibrium codes infer a flux map and calculate the shape and geometry of the plasma boundary and its distance to a known reference (e.g. first wall). These are inputs to one or more controllers capable of acting on the shape and trajectory based in pre-defined requests.Depending on the device, the error of the estimated boundary distance must usually be less than 1 centimetre, which translates into very small errors on the magnetic measurement itself. Moreover, asymmetries in the plasma generated and surrounding magnetic fields can produce local shape deformations potentially leading to an unstable control of the plasma geometry.The JET tokamak was recently upgraded to a new and less thermally robust all-metal wall, also known as the ITER-like wall. Currently the shape controller system uses the output of a single reconstruction algorithm to drive the plasma geometry and the protection systems have no input from the plasma boundary reconstruction. These choices are historical and were due to architectural, hardware and processing power limitations.Taking advantage of new multi-core systems and of the already proved robustness of the JET real-time network, this paper proposes a distributed architecture for the real-time identification of faults in the magnetic measurements of the JET tokamak. Besides detecting simple faults, such as short-circuits and openloops, the system compares the expected measurement at the coil location and the real measurement, producing a confidence value. Several magnetic reconstructions, using sensors from multiple toroidally distributed locations, can run in parallel, allowing for a voting or averaging scheme selection. Finally, any fault warnings can be directly fed to the real-time protection sequencer system, whose main function is to coordinate the protection of the JET's first wall.

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“…Recently this situation has changed. Papers about new approaches for the Tore Supra event and exception handling in long discharges [2,3] or the JET protection concept to protect the ITER like wall [4] describe issues characterized by complex relationships requiring non-trivial solutions. The ASDEX Upgrade methodology has also attracted attention in this context because of the effectiveness and versatility of its principles.…”

Section: Introductionmentioning

confidence: 99%

Event detection and exception handling strategies in the ASDEX Upgrade discharge control system

Treutterer¹,

Neu²,

Rapson³

et al. 2013

Fusion Engineering and Design

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Thermonuclear plasmas are governed by nonlinear characteristics: plasma operation can be classified into scenarios with pronounced features like L and H-mode, ELMs or MHD activity. Transitions between them may be treated like discrete events. Similarly, technical systems are also subject to discrete events such as failure of measurement sensors, actuator saturation or violation of machine and plant operation limits.Such situations often are handled with a mixture of pulse abortion and iteratively improved pulse schedule reference programming. In case of protection-relevant events the complexity of even a medium-sized device as ASDEX Upgrade, however, requires a sophisticated and coordinated shutdown procedure rather than a simple stop of the pulse. The detection of discrete events and their intelligent handling by the control system has been shown to be valuable also in terms of saving experiment time and cost. This paper outlines how ASDEX Upgrade's discharge control system (DCS) detects and deals with changes in the discrete system state. DCS performs event detection and exception handling in two stages: locally and centrally. The goal of local handling is to limit the effect of an exception to a minimal part of the controlled system. Thus, local exception handling facilitates robustness to failures but keeps the decision structures lean. A central state machine deals with exceptions requiring coordinated action of multiple control components. DCS implements the state machine by means of pulse schedule segments containing pre-programmed waveforms to define discharge goal and control method within a time window. Segments also include conditions to define exceptions and associated segment branching instructions. Thus, the state machine logic can automatically adapt to plasma and plant state.

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A generic method for real time detection of magnetic sensor failure on tokamaks

Cited by 10 publications

References 10 publications

Imputation of faulty magnetic sensors with coupled Bayesian and Gaussian processes to reconstruct the magnetic equilibrium in real time

Imputation of faulty magnetic sensors with coupled Bayesian and Gaussian processes to reconstruct the magnetic equilibrium in real time

A real-time architecture for the identification of faulty magnetic sensors in the JET tokamak

Event detection and exception handling strategies in the ASDEX Upgrade discharge control system

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