Automatic Control
NIU Yuguang, HE Qingbo, LI Yongsheng, CHEN Yanqiao, ZHANG Wenliang, FAN Guochao
A multi-input multi-output mathematical model was established using neural network algorithm for the coordinated control system of a supercritical thermal power unit, based on which, the feedforward, foreseeable and predictive method were used to optimize the control of the unit, thus to form a multivariable feedforward foreseeable predictive (FFP) control system. Results show that the neural network predictive control enables the unit to track the AGC curve, the sliding pressure target value and the midpoint temperature set value more quickly and accurately, whereas the feedforward control aggravates the fluctuation of the main steam pressure and the intermediate point temperature, while the foreseeable control has the effect of making these two indicators tend to be stable. Therefore, using the feedforward foreseeable predictive (FFP) algorithm to control the supercritical thermal power unit could improve the AGC response speed, the economy, the stability and the safety of the unit.