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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,
Universidad del Turabo,
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

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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Figures

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Fig. 1

HyPer facility schematic overview

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Fig. 2

Block diagram of HyPer MPC control application

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Fig. 3

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

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Fig. 4

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

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Fig. 5

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

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Fig. 6

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

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Fig. 7

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

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Fig. 8

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

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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

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Fig. 10

HyPer data used for state-space identification

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Fig. 11

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

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Fig. 12

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

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Fig. 13

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

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Fig. 14

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

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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

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Fig. 16

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

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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

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