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Research Papers

# Recursive System Identification and Simulation of Model Predictive Control Based on Experimental Data to Control the Cathode Side Parameters of the Hybrid Fuel Cell/Gas Turbine

[+] Author and Article Information
Bernardo Restrepo

Department of Mechanical Engineering,
Gurabo 00778-3030, Puerto Rico
e-mail: restrepob1@suagm.edu

David Tucker

U.S. Department of Energy,
National Energy Technology Laboratory,
Morgantown, WV 26507-0880
e-mail: david.tucker@netl.doe.gov

Larry E. Banta

White Hat Engineering,
Morgantown, WV 26508
e-mail: larry.banta@gmail.com

Manuscript received October 28, 2016; final manuscript received May 27, 2017; published online June 21, 2017. Assoc. Editor: Robert J. Braun.

J. Electrochem. En. Conv. Stor. 14(3), 031004 (Jun 21, 2017) (16 pages) Paper No: JEECS-16-1145; doi: 10.1115/1.4036944 History: Received October 28, 2016; Revised May 27, 2017

## Abstract

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 excursion dynamic data collected between the setup changes of the actuators on the cathode side of the HyPer facility were processed offline to determine the feasibility of applying an adaptive model predictive control strategy. Bypass valves along with electric load (EL) of the system were manipulated, and variables such as turbine speed, mass flow, temperature, pressure of the cathode side, among others were recorded for analysis. The three main phases of the MPC, identification of the models, control design, and control tuning have been described. Two identification structures, autoregressive exogenous (ARX) and a state-space model, were used to fit the measured data to dynamic models of the facility. The system identification ARX model required around 0.12 s of computer time. The state-space identification algorithm spent around 0.65 s, which was relatively high considering that the sample time of the sensors was 0.4 s. Visual inspection of the tracking accuracy showed 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 final prediction error (FPE) parameters, the state-space approach gives better results. For the ARX/state-space models, the MPC was robust in tracking setpoint variations. The MPC strategy described here offers potential to be the way to control the HyPer facility. 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. Constraints are highly present in the HyPer facility. The constraint airflow valves and the electric load were used in the simulation to control the constraint turbine speed and the cathode airflow (CAF). The MPC also displayed good disturbance rejection on the output variables when the fuel flow was set to simulate fuel cell (FC) heat effluent disturbances. Different off-design scenarios of operation were tested to confirm the estimated implementation behavior of the plant-controller dynamics. One drawback in MPC implementation is the computational time consuming between calculations and will be considered for future studies.

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## References

Tucker, D. , Lawson, L. O. , and Gemmen, R. S. , 2005, “ Characterization of Air Flow Management and Control in a Fuel Cell Turbine Hybrid,” ASME Paper No. PWR2005-50127.
Tucker, D. , Smith, T. P. , and Lawson, L. O. , 2006, “ Characterization of Bypass Control Methods in a Coal-Based Fuel Cell Turbine Hybrid,” International Colloquium on Environmentally Preferred Advanced Power (ICEPAG), Newport Beach, CA, Sept. 5–8, Paper No. ICEPAG2006-24008.
Tucker, A. , Liese, R. , and Gemmen, R. S. , 2009, “ Determination of the Operating Envelope for a Direct Fired Fuel Cell Turbine Hybrid Using Hardware Based Simulation,” International Colloquium on Environmentally Preferred Advanced Power Generation (ICEPAG), Newport Beach, CA, Feb. 10–12, Paper No. ICEPAG2009-1021.
Liese, E. , Gemmen, R. , Smith, T. , and Haynes, C. , 2006, “ A Dynamic Bulk SOFC Model Used in a Hybrid Turbine Controls Test Facility,” ASME Paper No. GT2006-90383.
Shelton, M. , Celik, I. , Tucker, D. , Liese, E. , and Lawson, L. , 2005, “ A Transient Model of a Hybrid Fuel Cell/Gas Turbine Test Facility Using Simulink,” ASME Paper No. GT2005-68467.
Ferrari, M. , Tucker, D. A. , Liese, E. , Lawson, L. D. , Traverso, A. , and Massardo, A. , 2007, “ Transient Modeling of the NETL Hybrid Fuel Cell/Gas Turbine Facility and Experimental Validation,” ASME J. Eng. Gas Turbines Power, 129(4), pp. 1012–1019.
Tsai, A. , Banta, L. , Tucker, D. , and Gemmen, R. S. , 2010, “ Multivariable Robust Control of a Simulated Hybrid Solid Oxide Fuel Cell Gas Turbine Plant,” ASME J. Fuel Cell Sci. Technol., 7(4), p. 041008.
Restrepo, B. , Banta, L. E. , Tsai, A. J. , and Tucker, D. , 2010, “ Combination of a Nonlinear Static and a Linear Dynamic Model of the NETL HyPer System,” ASME Paper No. FuelCell2010-33111.
Jurado, F. , and Saenz, J. , 2003, “ Adaptive Control of a Fuel Cell-Microturbine Hybrid Power Plant,” IEEE Trans. Energy Convers., 18(2), pp. 342–347.
Stiller, C. , Throud, B. , Bolland, O. , Kandepu, R. , and Imsland, L. , 2006, “ Control Strategy for a Solid Oxide Fuel Cell and Gas Turbine Hybrid System,” J. Power Sources, 158(1), pp. 303–315.
Xu, M. , Wang, Ch. , Qiu, Y. , Lu, B. , Lee, F. , and Kopasakis, G. , 2006, “ Control and Simulation for Hybrid Solid Oxide Fuel Cell Power Systems,” 21st Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Dallas, TX, Mar. 19–23, pp. 1269–1276.
Roberts, R. A. , and Brower, J. , 2006, “ Dynamic Simulation of a Pressurized 220 kW Solid Oxide Fuel Cell-Gas Turbine Hybrid System: Modeled Performance Compared to Measured Results,” ASME J. Fuel Cell Sci. Technol., 3(1), pp. 18–25.
Mueller, F. , Jabbari, F. , Brouwer, J. , Roberts, R. , Junker, T. , and Ghezel-Ayagh, H. , 2007, “ Control Design for a Bottoming Solid Oxide Fuel Cell Gas Turbine Hybrid System,” ASME J. Fuel Cell Sci. Technol., 4(3), pp. 221–230.
Mueller, F. , Jabbari, F. , Brouwer, J. , Junker, T. , and Ghezel-Ayagh, H. , 2006, “ Linear Quadratic Regulator for a Bottoming Solid Oxide Fuel Cell Gas Turbine Hybrid System,” International Colloquium on Environmentally Preferred Advanced Power (ICEPAG), Newport Beach, CA, Sept. 5–8, Paper No. ICEPAG2006-24018.
Tsai, A. , Banta, L. , Tucker, D. , and Lawson, L. , 2007, “ Determination of an Empirical Transfer Function of a Solid Oxide Fuel Cell Gas Turbine Hybrid System Via Frequency Response Analysis,” ASME Paper No. FC2007-25099.
Tsai, A. , Banta, L. , Tucker, D. , and Gemmen, R. , 2010, “ RGA Analysis of a Solid Oxide Fuel Cell Gas Turbine Hybrid Plant,” ASME J. Fuel Cell Sci. Technol., 7, pp. 675–680.
Tsai, A. , 2007, “ Multivariable Robust Control of a Simulated Hybrid Solid Oxide Fuel Cell Gas Turbine Plant,” Ph.D. dissertation, West Virginia University, Morgantown, WV.
Tsai, A. , Tucker, D. , and Emami, T. , 2014, “ Adaptive Control of a Nonlinear Fuel Cell-Gas Turbine Balance of Plant Simulation Facility,” ASME Paper No. FuelCell2014-1049.
Cutler, C. R. , and Ramaker, B. L. , 1980, “ Dynamic Matrix Control—A Computer Control Algorithm,” Joint Automatic Control Conference, San Francisco, CA, Aug. 13–15, Paper No. WP5-B.
Richalet, J. , 1993, “ Industrial Applications of Model Based Predictive Control,” Automatica, 29(5), pp. 1251–1274.
Clarke, D. W. , 1988, “ Application of Generalized Predictive Control to Industrial Processes,” IEEE Control Syst. Mag., 8(2), pp. 49–55.
Clarke, D. W. , 1991, “ Adaptive Generalized Predictive Control,” Chemical Process Control—CPC IV, Y. Arkun and W. H. Ray , eds., American Institute of Chemical Engineers, New York, pp. 395–417.
Belda, K. , and Böhm, J. , 2007, “ Input-Output Formulation of Multidimensional Adaptive Predictive Control,” Model Predictive Control, AT&P Journal PLUS2, Institute of Control Sciences, Moscow, Russia.
Eker, S. A. , and Nikolaou, M. , 2002, “ Simultaneous Model Predictive Control and Identification Closed-Loop Properties,” AIChE J., 48(9), pp. 1957–1980.
Krolikowski, A. , 1999, “ Adaptive Generalized Predictive Control Subject to Input Constraints,” Seventh Mediterranean Conference on Control and Automation (MED), Haifa, Israel, June 28–30, pp. 374–387.
Kemna, A. H. , Larimore, W. E. , Seborg, D. E. , and Mellichamp, D. A. , 1994, “ On-Line Multivariable Identification and Control of Chemical Processes Using Canonical Variate Analysis,” American Control Conference (ACC), Baltimore, MD, June 29–July 1, pp. 1650–1654.
Restrepo, B. , Banta, L. E. , and Tucker, D. , 2011, “ Characterization of a Solid Oxide Fuel Cell Gas Turbine Hybrid System Based on a Factorial Design of Experiments Using Hardware Simulation,” ASME Paper No. FuelCell2011-54146.
Ljung, L. , 1987, System Identification: Theory for the User, Prentice-Hall, Upper Saddle River, NJ.

## Figures

Fig. 1

HyPer facility schematic overview

Fig. 2

Block diagram of HyPer MPC control application

Fig. 3

Comparison between model versus measured outputs and the input dynamics ending at t = 60 s

Fig. 4

Comparison between model versus measured outputs and the input dynamics ending at t = 60.8 s

Fig. 5

Comparison between model versus measured outputs and the input dynamics ending at t = 61.6 s

Fig. 6

Comparison between model versus measured outputs and the input dynamics ending at t = 62.4 s

Fig. 7

MPC input and output dynamics with some setpoint variation using default values of matlab weighted matrices (without anticipation) using the ARX model

Fig. 8

MPC input and output dynamics with some setpoint variation after tuning the controller weighted matrices (without anticipation) using the ARX model

Fig. 9

MPC input and output dynamics with some setpoint variation after tuning the controller and using the MPC anticipation feature and the ARX model

Fig. 10

HyPer data used for state-space identification

Fig. 11

Comparison between online state-space identification data and measured output (time 1 and 2)

Fig. 12

Comparison between online state-space identification data and measured output (time 3 and 4)

Fig. 13

Comparison between online state-space identification data and measured output (time 5 and 6)

Fig. 14

Comparison between online state-space identification model and measured output (time 7 and 8)

Fig. 15

MPC input and output dynamics with some setpoint variation using default values of matlab weighted matrices (without anticipation) and the state-space model

Fig. 16

MPC inputs and outputs dynamics with some setpoint variation after tuning the controller weighted matrices (without anticipation) using the state-space model

Fig. 17

MPC inputs and outputs dynamics with some setpoint variation after tuning the controller and using the MPC anticipation feature and the state-space model

## Errata

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