Transfer Function Model Identification Based on Improved Least Square Method

The thermal system of nuclear power units is complicated and difficult to control. The simulation system plays an important role in the design of the control system. The model of the equipment is the basis of the simulation system. It is a difficult task to model a large number of equipment. In order to improve the modeling efficiency, this paper proposes a modeling method based on the actual operating data of the devices, which can identify the transfer function model of the devices through the data. Conventional least squares can identify the order of the model, but there are deficiencies in parameter optimization, which leads to poor identification accuracy. The particle swarm optimization algorithm has a better parameter optimization function, which can make up for the shortcomings of the least square method. Therefore, this paper combines the least square method and the improved PSO, and proposes a new transfer function model identification method, which effectively improves the accuracy of model identification.