Internal Model Control Strategy Of Main Steam Temperature System And Its Engineering Realization

The main steam temperature of thermal power plants is of great significance to the safety,stability and economic operation of the generating units.However,the system often has the characteristics of large delay,large inertia,nonlinearity and uncertain parameters.With the increase of capacity,the inertial characteristics significantly enhanced,accused of more complex objects.However,the traditional PID control can not adjust large delayed inertia objects in a timely manner,which often results in a large overshoot.Moreover,the controller with fixed parameters is not ideal for the control of uncertain parameters.Therefore,the internal model multi-model control of the main steam temperature system is put forward based on the idea of internal model control and multi-model control.This dissertation firstly introduces the basic theory of internal model control and multi-model control,proposes two structures of internal model multi-model control,that is,weight-based control strategy and switch-based control strategy and designs a new algorithm based on Bayesian probability weighting Internal model multi-model control algorithm.Based on the analysis of the dynamic characteristics of main steam temperature and the control scheme of spray water temperature reduction,a particle swarm optimization algorithm was used to model the main steam temperature in four typical operating conditions of a 600 MW supercritical simulation unit to form a model of the control system set.And use the inner model control principle to design the controller set.The internal model multi-model control algorithm is applied to the system in MATLAB.The simulation results show that the algorithm has faster response speed,shorter adjustment time and stronger anti-interference ability than the conventional PID control.At the same time,in order to verify the control effect of the algorithm in the industrial process,this dissertation realizes the communication between MATLAB and the simulator by composing communication program and proves that this control algorithm also has better performance in the simulator which is closer to the scene environment through a series of experiments performance.