Abstract
A supercritical coal-fired power generating unit is a typical multivariable system with large inertia and non-linear, slow time-variant, time-delay characteristics, which often makes the coordinated control quality deteriorate under wide-range load-changing conditions, and can't well satisfy the unit load and main steam pressure control requirements. Thus, it is of vital significance to study the supercritical boiler units operation characteristic by means of modeling method, and to improve the control quality with model-based advanced control strategies. In this paper, artificial neural network (ANN) method was used to build a nonlinear mathematical model of the load and main steam pressure characteristics for a 600MW supercritical boiler unit. Operation data over wide-range load-changing conditions were used for model training. Simulation tests showed that the model can fit the complex non-linear, dynamic characteristics between the units load, main steam pressure and fuel, feedwater flow and turbine governing valve opening with high precision and strong generalization ability. The model can be used as a prediction model to construct an intelligent controller for supercritical boiler unit coordinated control to meet the engineering application demand.
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