Load model of high power magnet power supply for EAST based on GMPSO-BP neural network

Abstract

To provide an accurate dynamic load model for the stability analysis of Tokamak, a method of back propagation (BP) neural network employing particle swarm optimization(PSO) algorithm based on mutation of Gaussian white noise disturbance (GMPSO-BP) is recommended. This method performs high-precision fitting using the GMPSO-BP neural network on the measured data of the experimental advanced superconducting Tokamak (EAST) poloidal field magnet power supply, extracts network parameters to build a dynamic load model, and then compares the simulation results with the measured data to calculate the error coefficient. The model is a component of the EAST distribution network's digital simulation model. An analogous load model is employed instead of developing the poloidal field magnet power supply from the mechanism. The revised algorithm has quick convergence times, good initial value adaptability, and error function accuracy of 0.3 to 3 %. The simulation outcomes show that the approach has a greater training effect.