Application Of Neural Network Control Technology In Modern Power Station

There are many plants that have the character of time-varying,large delay,large inertia in the process of production of modern power station such as the superheated steam temperature,the reheated steam temperature,the water treatment of boiler and the load regulation.Some of them have the strong character of nonlinear and some of them are multivariable coupling systems It is very difficult to obtain good effect of control by conventional PID control.It is necessary to set up the mathematics models of controlled plants if applying control means of modern control theory such as self-adaptive control,optimal control,decoupling control and predictive control.And these control systems have large calculation and bad character of real time in general.These disadvantages make them be not able to meet the need of real production process and limit the application of them in modern power station.Now the generator units are developing towards large capacity and high parameters.Many new kinds of generating electricity means are coming into being.The characers of production units of power station become more and more complex but the demand to quality of control becomes more and more strict.The new control means are in bad need to control them effectively.Neural network has the ability of learning and expressing any nonlinear relation.It can approximate any continuous function and their any order derivatives with any precision if it has the correct layers of networks and the correct number of hidden units.So the identification of dynamic character of time-varying,nonlinear plants has a kind of simple and effective means.The critical problem of control of time-varying,nonlinear plants is resolved.So all kinds of advanced control means based on neural network is an effective way that resolve the control problem of modern power station.This dissertation is dedicated to solve the practical control problems in power station under the existence of thermal control system characterized by large delay and time varying.Aiming at the present disadvantages of the neural network control algorithm,some improved means are researched and put forward.They are implicit generalized predictive control based on Elman network,self-adaptive predictive function control based on improved Elman network,nonlinear self-adaptive predictive functioncontrol based no hybrid neural network,multivariable decoupling self-adaptive predictive function control based on neural network and model reference self-adaptive predictive control based on fuzzy neural network.These means do not need the mathematic model of controlled plants but own the merits of self-adaptive control and predictive control of modern control theory .And the control effects are good and the adaptive character of them are extensive.They can meet the demand of production units with different characters of nonlinear plants and multivariable coupling systems.The simulation experiments of different plants such as superheated steam temperature, reheated steam temperature,water-supplying system,water treatment of boiler,load regulation are done in MATLAB.The results show that these control means are effective.