Liu Changliang

Nonlinear boiler model of 300 MW power unit for system dynamic performance studies

The nonlinear dynamic boiler model of 300 MW power unit is given which is derived from a mass and energy balance relationship. The parameters of the model can be obtained from the physical dimensions and characteristics of the boiler. The inputs of the model are feedwater valve position, steam valve position, fuel valve position, and feedwater temperature, the outputs of the model are drum pressure, water level, main steam flow. The model can be used for synthesis of model-based control algorithms of a boiler system. It can also be used for setting up a real-time simulator for testing a new boiler control system and operator training.

The application of genetic algorithm in model identification

A kind of improved genetic algorithm for identifying transfer function of thermal process in power plant is introduced. In the algorithm, floating-point coding, rank-based selection, elitist reservation and grouping method are used, the premature convergence is restrained, the global and local searching ability is improved. The genetic algorithm-based model identification MATLAB program is designed, the transfer functions of thermal process can be got with it according to the operating data log files. The identification results to topical thermal process is given. It is shown by simulation research that accurate identification results can be got no matter what kind of input signal is used, such as step signal, random operating signal, even if there is a strong noise in the input signal.

Model identification of thermal process in power plant

The identification methods of industrial process we discussed. The applied MATLAB program of least squares method is given and used to identify pulse transfer function. A kind of improved genetic algorithm is introduced to identify the transfer function and parameters of nonlinear dynamic model of thermal process. In the algorithm, floating-point coding, rank-based selection, elitist reservation and grouping method are used, the premature convergence is restrained, and the global and local searching ability is improved. With the genetic algorithm, the transfer function of topical thermal process can be identified accurately. The parameters of nonlinear model can be modified according to operating data of power plant, no matter what kind of input signal is used, such as step signal, random operating signal.

Prediction of solar radiation intensity in clear sky based on AOD estimation model

Atmospheric aerosol is one of the most important factors that cause the random variation of solar radiation intensity. In view of the problem that the atmospheric aerosol optical depth (AOD) is difficult to obtain real-timely with high accuracy, BP neural network method is adopted to estimate the AOD based on PM2.5 concentration, PM10 concentration, air temperature and air relative humidity that from air quality monitoring station; then, take AOD and precipitable water as main change parameters, REST2 model is simplified to calculate direct solar radiation intensity and scattered radiation intensity in clear sky. Experimental results show that the proposed method for predicting solar radiation intensity in clear sky is of high precision and easy to be realized.

PSO and RBF network-based Wiener model and its application to system identification

In this paper, a new kind of Wiener model structure is introduced, which is realized by using the mapping function of neural networks. The model uses the linear dynamic neurons and a RBF network to express one Wiener models dynamic linear part and static nonlinear part respectively. The parameter identification for the new Wiener model adopts the unified identification method. The learning of parameters includes two cycles that the inner-cycle is executed by gradient training methods based on the BP thought and the outer-cycle uses the PSO (Particle Swarm Optimization) algorithm. The training method based on unified identification makes the new Wiener model converge to the steady state along the expected direction with a small error in a short time. The Wiener model is applied to the identification of the famous Box and Jenkins gas CO2 density, and the simulation results show that the method proposed in this paper is effective.

Nonlinear modeling and simulation for large scale coal-fired power unit

On the basis of analysis to the boiler models that have exist, the modeling method and simulation environment for large-scale coal-fired power unit are discussed. The power unit is divided into many parts; a set of dynamic model library of thermal equipment and subsystem is setup on with modularization modeling method. The simulation environment is selected as MATLAB/simulink and S-function is used. The integrated dynamic model of 300 MW coal-fired power unit is set up in simulink.

Self-tuning Fuzzy Control of MIMO System and its Application

ABSTRACT Against the serious coupling among system variables, long time delay of outputs response and time changing of model of the low speed pulverizer of power plant, a kind of self-tuning fuzzy control algorithm is presented. In the algorithm, the coupling among system variables has been eliminated, the proportion of the deviation and its differential can be adjusted automatically according to the control process, and accurate object model is not needed when setting the controller parameters. The fuzzy control algorithm in this paper has been used into power plant and satisfactory control result acquired.