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With the development of new energy, the primary frequency control (PFC) is becoming more and more important and complicated. To improve the reliability of the PFC, an evaluation method of primary frequency control ability (PFCA) was proposed. First, based on the coupling model of the coordinated control system (CCS) and digital electro-hydraulic control system (DEH), principle and control mode of the PFC were introduced in detail. The simulation results showed that the PFC of the CCS and DEH was the most effective control mode. Then, the analysis of the CCS model and variable condition revealed the internal relationship among main steam pressure, valve opening and power. In term of this, the radial basis function (RBF) neural network was established to estimate the PFCA. Because the simulation curves fit well with the actual curves, the accuracy of the coupling model was verified. On this basis, simulation data was produced by coupling model to verify the proposed evaluation method. The low predication error of main steam pressure, power and the PFCA indicated that the method was effective. In addition, the actual data obtained from historical operation data were used to estimate the PFCA accurately, which was the strongest evidence for this method.

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.