Iterative noise removal from temperature and density profiles in the TJ-II Thomson scattering

Farías, Gonzalo; Dormido-Canto, S.; Vega, J.; Santos, Marco Antônio Cetale; Pastor, I.; Fingerhuth, Sebastian; Ascencio, J.A.

doi:10.1016/j.fusengdes.2014.03.050

Cited by 4 publications

(7 citation statements)

References 4 publications

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“…In future research the use of support vector machines (SVM) [ 23 , 25 ] and hybrid methods [ 26 , 27 , 28 ] will be explored by the authors in order to find a new algorithm with even higher performance. Furthermore, authors will try to study the nonlinear time varying systems and other power quality features, taken into account proposals like [ 29 , 30 ]. Finally, another research line that will be explored is the missing data imputation in the time-frequency domain.…”

Section: Discussionmentioning

confidence: 99%

A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers

Turrado

Lasheras

Calvo-Rolle

et al. 2015

Sensors

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Nowadays, data collection is a key process in the study of electrical power networks when searching for harmonics and a lack of balance among phases. In this context, the lack of data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, and current in each phase and power factor) adversely affects any time series study performed. When this occurs, a data imputation process must be accomplished in order to substitute the data that is missing for estimated values. This paper presents a novel missing data imputation method based on multivariate adaptive regression splines (MARS) and compares it with the well-known technique called multivariate imputation by chained equations (MICE). The results obtained demonstrate how the proposed method outperforms the MICE algorithm.

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Section: Discussionmentioning

confidence: 99%

A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers

Turrado

Lasheras

Calvo-Rolle

et al. 2015

Sensors

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“…The first phase is to construct a sliding surface to confine the system dynamics to a sliding manifold with the desired behavior. Let us consider the ideal disturbance-free tracking error expression (13) and suppose that the trajectory of the state has intercepted the sliding surface at 0 , and exists a sliding mode at ≥ 0 implying that = 0 anḋ= 0. Deriving with respect to time along the trajectory is defined by (13):…”

Section: Sliding Mode Control Schemementioning

confidence: 99%

“…Therefore, combining (13) and (17), given ( 0 ) = 0, the dynamics of the system on the sliding surface for ≥ 0 arė = − = 0.…”

Section: Sliding Mode Control Schemementioning

confidence: 99%

“…The TCV reactor, despite its small size of 1.54 m height by 0.56 width, is a very complex machine, fully loaded of sensors (the so-called diagnostics) and actuators, as it can be seen in Figure 3. Among the diagnostics, there are some for the measurement of the spatial profiles of the electron temperature and density, such as the Thomson Scattering Diagnostic [13], or for the measurement for the plasma currents, temperature, density, and potential, such as the Langmuir probes [14], whose locations are illustrated in Figure 4. The data obtained from some diagnostics (as well as many others related to the plasma physics) are processed and serve as input for the control system, which can actuate over different types of actuators, the Electron Cyclotron Resonance Heating and Current Drive System (ECRH-ECCD), the gas valves, and the coils.…”

Section: Introductionmentioning

confidence: 99%

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A Variable Structure Control Scheme Proposal for the Tokamak à Configuration Variable

et al. 2019

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Fusion power is the most significant prospects in the long-term future of energy in the sense that it composes a potentially clean, cheap, and unlimited power source that would substitute the widespread traditional nonrenewable energies, reducing the geographical dependence on their sources as well as avoiding collateral environmental impacts. Although the nuclear fusion research started in the earlier part of 20th century and the fusion reactors have been developed since the 1950s, the fusion reaction processes achieved have not yet obtained net power, since the generated plasma requires more energy to achieve and remain in necessary particular pressure and temperature conditions than the produced profitable energy. For this purpose, the plasma has to be confined inside a vacuum vessel, as it is the case of the Tokamak reactor, which consists of a device that generates magnetic fields within a toroidal chamber, being one of the most promising solutions nowadays. However, the Tokamak reactors still have several issues such as the presence of plasma instabilities that provokes a decay of the fusion reaction and, consequently, a reduction in the pulse duration. In this sense, since long pulse reactions are the key to produce net power, the use of robust and fast controllers arises as a useful tool to deal with the unpredictability and the small time constant of the plasma behavior. In this context, this article focuses on the application of robust control laws to improve the controllability of the plasma current, a crucial parameter during the plasma heating and confinement processes. In particular, a variable structure control scheme based on sliding surfaces, namely, a sliding mode controller (SMC) is presented and applied to the plasma current control problem. In order to test the validity and goodness of the proposed controller, its behavior is compared to that of the traditional PID schemes applied in these systems, using the RZIp model for the Tokamak à Configuration Variable (TCV) reactor. The obtained results are very promising, leading to consider this controller as a strong candidate to enhance the performance of the PID-based controllers usually employed in this kind of systems.

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“…There are some classical solutions (e.g., notch laser-line rejection filters) that could be applied to reduce the noise; however, since the shape of the noise is not regular, the results with these solutions are not suitable. On the contrary, some advanced image processing algorithms have shown very good results when trying to reduce the noise without affecting most of the acquired data [12,13].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Models to Reduce Stray Light in TJ-II Thomson Scattering Diagnostic

Correa,

Farias,

Fabregas

et al. 2024

Sensors

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Nuclear fusion is a potential source of energy that could supply the growing needs of the world population for millions of years. Several experimental thermonuclear fusion devices try to understand and control the nuclear fusion process. A very interesting diagnostic called Thomson scattering (TS) is performed in the Spanish fusion device TJ-II. This diagnostic takes images to measure the temperature and density profiles of the plasma, which is heated to very high temperatures to produce fusion plasma. Each image captures spectra of laser light scattered by the plasma under different conditions. Unfortunately, some images are corrupted by noise called stray light that affects the measurement of the profiles. In this work, we propose the use of deep learning models to reduce the stray light that appears in the diagnostic. The proposed approach utilizes a Pix2Pix neural network, which is an image-to-image translation based on a generative adversarial network (GAN). This network learns to translateimages affected by stray light to images without stray light. This allows for the effective removal of the noise that affects the measurements of the TS diagnostic, avoiding the need for manual image processing adjustments. The proposed method shows a better performance, reducing the noise up to 98% inimages, which surpassesprevious works that obtained 85% for the validation dataset.

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Iterative noise removal from temperature and density profiles in the TJ-II Thomson scattering

Cited by 4 publications

References 4 publications

A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers

A New Missing Data Imputation Algorithm Applied to Electrical Data Loggers

A Variable Structure Control Scheme Proposal for the Tokamak à Configuration Variable

Deep Learning Models to Reduce Stray Light in TJ-II Thomson Scattering Diagnostic

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