Study of a Multivariable Coordinate Control for a Supercritical Power Plant Process

the paper presents our recent research work in study of a novel multivariable coordinate control for a 600MW supercritical (SC) power plant process. The nonlinear mathematical model of the plant is briefly described in the paper. Then, a control strategy is designed which is based on Generalized Model Predictive Control (MPC) theory and tested within small load changes. But the MPC alone performs well only within small load changes even with consideration of constant disturbance and measurement noises in prediction algorithm. We then propose a dynamic compensator in parallel with the MPC to track large load changes. Because the model has been identified with on-site closed loop data, the multivariable optimal control signals have been used as a correction to the reference of the plant local controls instead of direct control signal applications. The simulation results have shown good performance of the controller in response to some load changes. Furthermore, it has been proved that the plant dynamic response can be improved by increasing the coal grinding capability and pulverized coal discharging through the implemented coal mill controllers. The benefit from this study is to provide convinced methodology to improve the SC power plant dynamic response for fulfilment of National Grid Code (NGC) requirements.