Model predictive control of a Brayton cycle based power plant

Abstract

The aim of this study is to implement the model predictive control in order to optimally control the power output of a Brayton cycle based power plant. Other control strategies have been tried but there still exists the need for better performance. In real systems, a number of constraints exist. Incorporating these into the control design is no trivial task. Unlike in most control strategies, model predictive control allows the designer to explicitly incorporate constraints in its formulation. The original design of the PBMR power plant is considered. It uses helium gas as the working fluid. The power output of the system can be controlled by manipulating the helium inventory to the gas cycle. A linear model of the power plant, modelled in Simulink® is used. This linear model is used as an evaluation platform for the control strategy. The helium inventory is manipulated by means of actuators which use values generated by the controller. The controller computes these values by minimizing the cost of future outputs over a finite horizon in the presence of constraints. The dynamic response of the system is used to tune the controller. The power output performance at different configurations of the controller under perfect conditions and with disturbances is examined. The best configuration is used resulting in an optimal power control system for the Brayton cycle based power plant. Results showed that the method employed can be used to implement the control strategy. Furthermore, better performance can be realised with model predictive control.