Prediction for Tokamak High-power Pulse Load based on Elman Neural Network

Abstract A dynamic load prediction model of the Elman neural network is suggested to increase reliability tokamak high-power pulse prediction. The method enhances network’s capacity process input by employing a back-propagation algorithm. comparison simulation results and measured data, depending on data EAST (the experimental advanced superconducting tokamak) power bus, shows that are more accurate than traditional BP network, which can describe impact fusion device’s pulsed grid better precision stability analysis.