Supercritical Power Unit Load Control And Optimization Based On Generalized Predictive Control

Power unit is the major equipment in modern electricity enterprises, particularly high parameter and high capacity power unit accounting for larger and larger proportion for its high unit efficiency. Energy saving and emission reduction of thermal power plant is the key of power industry, which target to cut down coal consumption and enhance competitive of the company. The supercritical power unit load process is nonlinear, time-variation and multivariable coupling. As the difference dynamics, coordinated control of boiler and turbine should be considered based on well control of them separately. The main works of the thesis are as follows:1. The principle and characteristics of power unit are briefly introduced. The automatic control systems for supercritical power unit is described. The research and application of advanced control and optimization for power unit at home and abroad are summarized and contrasted.2. The dynamics of supercritical power unit and mechanism of its components are analyzed and its modeling research status is presented. The main manipulated variables and controlled outputs of supercritical power unit load process are introduced as well as control objects. A detailed analysis of multivariable generalized predictive control and optimization strategy and its constraints dealing is given.3. A generalized predictive control and optimization strategy in order to minimize coal consumption is proposed according to the characteristics of supercritical power unit load process. Goal programming and linear programming is employed in local steady state optimization respectively. The simulation results demonstrate that, in the case of no need for a target coordination, GP+GPC and LP+GPC show same performance for both lower coal consumption while assuring the steady of superheated temperature and pressure before turbine and show some robust while model mismatch compared to only GPC.When a target coordination is required, the dynamic control and local economic steady state optimization could be coordinated based on GP+GPC.4. A soft sensoring of carbon content in fly ash and slag which are the indices of boiler efficiency is modeled using least squared support vector machine, so as to achieve steady state control and optimization oriented to power unit efficiency. The particle swarm optimization based on natural selection is then employed to optimize parameters of LSSVM in modeling. The simulation data from industrial field demonstrates accuracy. It is the basic for steady state optimization oriented to boiler efficiency.5. The last part is summary and outlook.