Plant-wide predictive control for a thermal power plant based on a physical plant model

A constrained nonlinear, physical model-based, predictive control (NPMPC) strategy is developed for improved plant-wide control of a thermal power plant. The strategy makes use of successive linearisation and recursive state estimation using extended Kalman filtering to obtain a linear state-space model. The linear model and a quadratic programming routine are used to design a constrained long-range predictive controller. One special feature is the careful selection of a specific set of plant model parameters for online estimation, to account for time-varying system characteristics resulting from major system disturbances and ageing. These parameters act as nonstationary stochastic states and help to provide sufficient degrees-of-freedom to obtain unbiased estimates of controlled outputs. A 14th order nonlinear plant model, simulating the dominant characteristics of a 200 MW oil-fired power plant has been used to test the NPMPC algorithm. The results compare favourably to those obtained with the state-space GPC method designed under similar conditions