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Research Papers

Multiple-Model Adaptive Control of a Hybrid Solid Oxide Fuel Cell Gas Turbine Power Plant Simulator

[+] Author and Article Information
Alex Tsai

United States Coast Guard Academy,
New London, CT 06320

Paolo Pezzini, Kenneth M. Bryden

Ames Laboratory,
Iowa State University,
Ames, IA 50011

David Tucker

U.S. Department of Energy,
National Energy Technology Laboratory,
Morgantown, WV 26505

Manuscript received May 30, 2018; final manuscript received December 19, 2018; published online February 19, 2019. Assoc. Editor: Vittorio Verda. The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States Government purposes.

J. Electrochem. En. Conv. Stor. 16(3), 031003 (Feb 19, 2019) (11 pages) Paper No: JEECS-18-1050; doi: 10.1115/1.4042381 History: Received May 30, 2018; Revised December 19, 2018

A multiple model adaptive control (MMAC) methodology is used to control the critical parameters of a solid oxide fuel cell gas turbine (SOFC-GT) cyberphysical simulator, capable of characterizing 300 kW hybrid plants. The SOFC system is composed of a hardware balance of plant (BoP) component, and a high fidelity FC model implemented in software. This study utilizes empirically derived transfer functions (TFs) of the BoP facility to derive the MMAC gains for the BoP system, based on an estimation algorithm which identifies current operating points. The MMAC technique is useful for systems having a wide operating envelope with nonlinear dynamics. The practical implementation of the adaptive methodology is presented through simulation in the matlab/simulink environment.

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References

Tucker, D. , Lawson, L. , and Gemmen, R. , 2003, “ Preliminary Results of a Cold Flow Test in a Fuel Cell Gas Turbine Hybrid Simulation Facility,” ASME Paper No. GT2003-38460.
Tucker, D. , Liese, E. , and Gemmen, R. , 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, Newport Beach, CA, Feb. 10–12, Paper No. ICEPAG2009-1021.
Tucker, D. , and Gemmen, L. Lawson, R. , 2005, “ Characterization of Air Flow Management and Control in a Fuel Cell Turbine Hybrid Power System Using Hardware Simulation,” ASME Paper No. PWR2005-50127.
Winkler, W. , Nehter, P. , Tucker, D. , Williams, M. , and Gemmen, R. , 2006, “ General Fuel Cell Hybrid Synergies and Hybrid System Testing Status,” J. Power Sources, 159(1), pp. 656–666. [CrossRef]
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Pezzini, P. , Banta, L. , Traverso, A. , and Tucker, D. , 2014, “ Decentralized Control Strategy for Fuel Cell Turbine Hybrid Systems,” 57th Annual ISA Power Industry Division Symposium, Scottsdale, AZ, June 1–6, Paper No. POWID2014-52.
Pezzini, P. , Celestin, S. , and Tucker, D. , 2015, “ Control Impacts of Cold-Air Bypass on Pressurized Fuel Cell Turbine Hybrids,” ASME J. Fuel Cell Sci. Technol., 12(1), p. 011006. [CrossRef]
Tsai, A. , Banta, L. , Tucker, D. , and Gemmen, R. , 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. [CrossRef]
Tsai, A. , Tucker, D. , and Groves, C. , 2010, “ Improved Controller Performance of Selected Hybrid SOFC-GT Plant Signals Based on Practical Control Schemes,” ASME J. Eng. Gas Turbines Power, 133(7), p. 071702. [CrossRef]
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Tsai, A. , Banta, L. , Tucker, D. , and Lawson, L. , 2009, “ Determination of an Empirical Transfer Function of a Solid Oxide Fuel Cell Gas Turbine Hybrid System Via Frequency Response Analysis,” ASME J. Fuel Cell Sci. Technol., 6(3), p. 034505. [CrossRef]
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Figures

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Fig. 1

NETL HyPer project

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Fig. 2

HyPer BoP test facility

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Fig. 3

Open-loop response for three operating points

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Fig. 4

Multiple model adaptive control Simulink model

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Fig. 5

Model of the full state feedback reference controller [2]

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Fig. 6

Switching mechanism for changing Op Pts

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Fig. 7

Simulink KF bank subsystem

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Fig. 8

Prediction type KF Simulink subsystem

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Fig. 9

Simulink controller subsystem

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Fig. 10

Probability equation in a Simulink subsystem

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Fig. 11

Closed-loop response: individual Op Pts and its controller

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Fig. 12

KF estimation effectiveness

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Fig. 13

Actuator CL response comparison for OP1 and OP2

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Fig. 14

Single controller response for multiple plant changes

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Fig. 15

System response to the MMAC algorithm

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Fig. 16

Actuator CL response of the MMAC

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Fig. 17

Multiple model adaptive estimation calculated residuals for the three OP's

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Fig. 18

Model probabilities for the response in Fig. 16

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Fig. 19

Probabilities of second plant switching sequence

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Fig. 20

Residuals for plant sequence no. 2

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Fig. 21

Closed-loop response of sequence no. 2

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Fig. 22

Actuator response for sequence no. 2

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