Plasma boundary detection and reconstruction are important not only for plasma operation but also for plasma facing materials. Traditional methods, for example, EFIT code, which is constrained by electromagnetic measurement, and is very challenging for detecting the plasma boundary in long-pulse burning plasma devices such as ITER. A novel algorithm for the reconstruction of the plasma boundary using one visible camera has been developed on experimental advanced superconducting tokamak (EAST) for fusion reactors. A U-Net convolutional neural network was used to identify the plasma boundary and the pixel coordinates of the boundary points were fitted with EFIT via the XGBoost model. This algorithm can transform the boundary from the image plane to the poloidal plane of the Tokamak based on machine learning without traditional spatial calibration, and then the reconstruction of the plasma configuration shall be realized based on a monocular visible light camera. The reconstruction accuracy of this algorithm is relatively high. The average error on the test set was only 7.36 mm (<1 cm) and satisfied the accuracy requirements of control for EAST tokamak. This result can contribute to the development of the plasma boundary reconstruction and operation based on one visible camera.
Plasma boundary reconstruction from reflectometer arrays