Real-Time Magnetic Sensor Anomaly Detection Using Autoencoder Neural Networks on the DIII-D Tokamak

Abstract

Magnetic diagnostics in tokamaks are key to plasma equilibrium control (plasma current, plasma shape, and position) and amelioration of plasma instabilities. Thus, real-time identification of the anomalous sensor is mandatory. A new real-time system based on autoencoder (AE) neural networks (NNs) for anomaly detection in magnetics signals, including both flux loops and magnetic probes, has been successfully implemented on the plasma control system (PCS) of the DIII-D tokamak. The AE NN is trained on over 4000 plasma discharges, with an optimized latent space representation of the input signals while minimizing the reconstruction loss. An algorithm determining anomalous sensors based on excessive deviations from accurate reconstruction from the trained NNs is constructed in a MATLAB/Simulink environment and is deployed on the real-time PCS using the embedded MATLAB coder (EMC) environment. The reconstruction performance of the algorithm is quantified in hardware-in-the-loop (HIL) simulation by injecting artificially common fault types, including stuck-at-zero fault, saturated fault, noise fault, and drift fault. The AE NN algorithm has been successfully commissioned on the DIII-D PCS.