This paper reports the development of a constrained, non-linear physical model-based, predictive control (NPMPC) strategy for improved plant-wide control of a thermal power plant. The proposed strategy makes use of successive linearisation and extended Kalman filtering (EKF) to obtain a linear state-space model. The linear model and a quadratic programming routine are then used to design a constrained long-range predictive control routine. The proposed approach places major emphasis on better disturbance modeling based on a physical plant model. The paper discusses how this approach results into selection of a specific set of model parameters for on-line estimation to account for time-varying system characteristics resulting from major system disturbances and ageing. A plant model with 14 non-linear ODEs, simulating the dominant characteristics of a 200 MW oil-fired power plant at Ballylumford, N. Ireland, has been used to test the control strategy. The simulation results demonstrate that the constrained NPMPC controller provides significantly faster disturbance rejection with realistic rates of changes in manipulated variables during large system disturbances and extremely high rate of load changes. Results also demonstrate that the constrained algorithm provides a fault-tolerant capability to the controller, while satisfying the system constraints for economical plant operation.
|Title of host publication||Unknown Host Publication|
|Number of pages||6|
|Publication status||Published - Dec 1999|
|Event||38th IEEE Conference on Decision and Control (CDC), December 7-10, Phoenix, Arizona, USA - Phoenix, Arizona, USA|
Duration: 1 Dec 1999 → …
|Conference||38th IEEE Conference on Decision and Control (CDC), December 7-10, Phoenix, Arizona, USA|
|Period||1/12/99 → …|