Predicting Overload Risk on Plasma-Facing Components at Wendelstein 7-X from IR Imaging Using Self-Organizing Maps

Giuliana Sias (), Emanuele Corongiu (), Enrico Aymerich, Barbara Cannas, Alessandra Fanni, Yu Gao, Bartłomiej Jabłoński, Marcin Jakubowski, Aleix Puig Sitjes, Fabio Pisano () and Team W7-X
Additional contact information
Giuliana Sias: Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
Emanuele Corongiu: Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
Enrico Aymerich: Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
Barbara Cannas: Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
Alessandra Fanni: Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
Yu Gao: Max-Planck-Institut für Plasmaphysik, 17491 Greifswald, Germany
Bartłomiej Jabłoński: Department of Microelectronics and Computer Science, Lodz University of Technology, 93-005 Lodz, Poland
Marcin Jakubowski: Max-Planck-Institut für Plasmaphysik, 17491 Greifswald, Germany
Aleix Puig Sitjes: Max-Planck-Institut für Plasmaphysik, 17491 Greifswald, Germany
Fabio Pisano: Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy
Team W7-X: Collaborators/Membership of the W7-X Team is provided in the Acknowledgments.

Energies, 2025, vol. 18, issue 12, 1-18

Abstract: Overload detection is crucial in nuclear fusion experiments to prevent damage to plasma-facing components (PFCs) and ensure the safe operation of the reactor. At Wendelstein 7-X (W7-X), real-time monitoring and prediction of thermal events are essential for maintaining the integrity of PFCs. This paper proposes a machine learning approach for developing a real-time overload detector, trained and tested on OP1.2a experimental data. The analysis showed that Self-Organizing Maps (SOMs) are efficient in detecting the overload risk starting from a set of plasma parameters that describe the magnetic configuration, the energy behavior, and the power balance. This study aims to thoroughly evaluate the capabilities of the SOM in recognizing overload risk levels, defined by quantizing the maximum criticality across different IR cameras. The goal is to enable detailed monitoring for overload prevention while maintaining high-performance plasmas and sustaining long pulse operations. The SOM proves to be a highly effective overload risk detector. It correctly identifies the assigned overload risk level in 87.52% of the samples. The most frequent error in the test set, occurring in 10.46% of cases, involves assigning a risk level to each sample adjacent to the target one. The analysis of the results highlights the advantages and drawbacks of criticality discretization and opens new solutions to improve the SOM potential in this field.

Keywords: overload detection; Self-Organizing Maps; Wendelstein 7-X; plasma-facing components; real-time monitoring; infrared diagnostics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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