Prediction of minor disruptions in Keda Torus eXperiment

This study investigates minor disruptions in the Keda Torus eXperiment (KTX) and presents a deep learning-based approach for disruption prediction. Plasma disruptions in magnetic confinement devices are critical to operational safety, making their prediction essential. Machine learning methods provide a foundation for predicting disruptions and informing mitigation strategies. Although the stabilizing shell in reverse-field pinch devices typically suppresses disruptions, minor disruptions occur frequently in KTX and are closely linked to plasma current center-of-gravity displacement. This work demonstrates, for the first time, the accurate and effective prediction of minor disruptions in a reverse-field pinch configuration. The KTX Recurrent Neural Network (KRNN) prediction framework, which integrates two-dimensional closed-boundary eddy-current diagnostic arrays, additional diagnostic methods, and long/short-term memory networks (LSTMs), enables early warning of minor disruptions and supports active feedback control. Notably, the two-dimensional closed-boundary electromagnetic probe array in KTX, which captures more spatial distribution information than one-dimensional arrays, extends the early warning time for minor disruptions by an average of 21.1%. The KRNN achieves a maximum prediction accuracy of 98%.