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

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.