A Model Predictive Control (MPC) strategy has been suggested and simulated with the empirical dynamic data collected on the Hybrid Performance (HyPer) project facility installed at the National Energy Technology Laboratory (NETL), U.S. Department of Energy, in Morgantown, WV. The HyPer facility is able to simulate gasifier/fuel cell power systems and uses hardware-based simulation approach that couples a modified recuperated gas turbine cycle with hardware driven by a solid oxide fuel cell model. Dynamic data was collected by operating the HyPer facility continuously during five days. Bypass valves along with electric load of the system were manipulated and variables such as mass flow, turbine speed, temperature, pressure, among others were recorded for analysis. This work was developed by focusing on a multivariable recursive system identification structure fitting measured transient data. The results showed that real-time or online data is a viable means to provide a dynamic model for controller design.

The excursion dynamic data collected between the setup changes of the experiments was processed off-line to determine the feasibility of applying an adaptive Model Predictive Control strategy. One of the strengths of MPC is that it can allow the designer to impose strict limits on inputs and outputs in order to keep the system within known safe bounds. Two identification structures, ARX and a State-Space model, were used to fit the measured data to dynamic models of the HyPer facility. The State-Space identification was very accurate with a second order model. Visual inspection of the tracking accuracy shows that the ARX approach was approximately as accurate as the State-Space structure in its ability to reproduce measured data. However, by comparing the Loss Function and the FPE parameters, the State-Space approach gives better results.

The MPC proved to be a good strategy to control the HyPer facility. The airflow valves and the electric load were used to control the turbine speed and the cathode airflow. For the ARX/State Space models, the MPC was very robust in tracking set-point variations. The anticipation feature of the MPC was revealed to be a good tool to compensate time delays in the output variables of the facility or to anticipate eventual set-point moves in order to achieve the objectives very quickly. The MPC also displayed good disturbance rejection on the output variables when the fuel flow was set to simulate FC heat effluent disturbances. Different off-design scenarios of operation have been tested to confirm the estimated implementation behavior of the plant-controller dynamics.

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