Real-time plasma horizontal displacement estimator based on the neural network

Abstract

Plasma displacement diagnostics is one of the basic diagnostics for evaluating the plasma operating state in tokamak devices. It is related to the plasma current centroid and generally obtained by integrating and calculating the induced voltage in the displacement coils. This induced voltage is derived from the magnetic field generated by the plasma. If the induced voltage is not solely due to the plasma magnetic field, there will be an unreal deviation in the plasma displacement. In J-TEXT, this phenomenon usually occurs when the Resonant Magnetic Perturbation (RMP) coils close to the displacement coils are energized. For long pulse tokamak devices, the source of the unreal deviation may be drifting integrators or radiation-induced voltages in the magnetic pickup coils, not only the RMP coils. To solve this problem, this paper proposes a real-time non-magnetic plasma horizontal displacement measurement method using a deep fully connected neural network. The soft-X ray (SXR) diagnostic data and the plasma displacement diagnostic data are used as the training data for the network. The training and testing results show that the network obtains a satisfactory fitting effect. On the other hand, to deploy this network for real-time diagnostics, this paper has designed and developed a plasma horizontal displacement estimator based on J-TEXT real-time framework (JRTF) in J-TEXT. The experimental results show that the real-time estimator is sufficient for real-time plasma displacement diagnostics. The feasibility of establishing real-time plasma displacement diagnostics based on the SXR diagnostic system has been verified.