A constrained non-linear, physical model-based, prcdictive control (NPMPC) strategy isdeveloped for improved plant-wide control of a thermal power plant. Thc strategy makes use ofsucccssive linearisation and recursive state estimation using cxtcnded Kalman filtering to obtain a linear state-space model. Thc linear model and a quadratic programming routine are used to design a constrained long-range predictive controllcr. 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 degrecs-of-freedom to obtain unbiased estimates of controllcd outputs. A 14th order non-linear plant modcl, simulating the dominant characteristics of a 200 MW oil-fired power plant has been used to test the NPMPC algorithm. The control strategy gives impressive simulation results, during Iargc system disturbances and extremely high ratc of load changes, right across the operating range. These results compare favourably to those obtaincd with the state-space GPC method designed under similar conditions.
|Journal||IEE Proceedings - Control Theory and Applications|
|Publication status||Published - May 2000|