The parameter identification method of steam turbine nonlinear servo system based on artificial neural network

Abstract

The servo system in steam turbine digital electric-hydraulic control system (DEH) is affected by nonlinear factors when it is working. To accurately simulate dynamic characteristics of the DEH, a new nonlinear servo system is proposed, which has limit, dead zone and correction coefficient caused by unknown factors. The model parameters are divided into linear parameters and nonlinear parameters to be identified, respectively. Neural networks are used to identify linear parameters. The nonlinear parameters should be identified according to flow characteristic curve. To verify the validity of the proposed model and parameter identification method, the actual data of primary frequency control from a 1000 MW Ultra Supercritical Unit is adopted. Meanwhile, the linear model with no nonlinear factors is used for comparison. Where the fitting degree of valve opening is 98.445% and power is 96.986%, the output of nonlinear model coincides with actual output well. Where the relative error of stable result is 5% of valve opening and 1.58% of power, the error of linear model is larger. The simulation results of the proposed method show that the nonlinear factors of high-power units cannot be ignored and the nonlinear model of servo system is more accurate.