The plant-level supervisory information system(SIS)in thermal power plant

as an information system taking optimization of unit operation and enhancement of economic efficiency in operation as the main targets

has played an important role in plant-level management in power plants.In order to more effectively utilize the vast amount of data on the SIS platform

an advanced control strategy

i.e.dynamic matrix predictive control

has been put forward.Taking SIS of one power plant in Hubei Province as example

and making temperature of superheated steam as the controlled object

and the flow rate of steam as the disturbance variable the variation of boiler's operating condition has been manifested.A dynamic matrix model has been established by using the neural network recognition method

and the dynamic matrix control algorithm being applied to the steam temperature control system.Result of emulation shows that the neural network dynamic model of superheated steam established on the basis of emulated data can perfectly predict dynamic property of the controlled object under different operating conditions.The dynamic matrix control strategy based on neural network model can provide better control effectiveness than that of traditional PID control.