Research Papers

Adaptive Control of a Nonlinear Fuel Cell-Gas Turbine Balance of Plant Simulation Facility

[+] Author and Article Information
Alex Tsai

United States Coast Guard Academy,
New London, CT 06320
e-mail: alex.j.tsai@gmail.com

David Tucker

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

Tooran Emami

United States Coast Guard Academy,
New London, CT 06320
e-mail: tooran.emami@uscga.edu

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF FUEL CELL SCIENCE AND TECHNOLOGY. Manuscript received April 20, 2014; final manuscript received May 26, 2014; published online September 16, 2014. Editor: Nigel M. Sammes.

This material is declared a work of the US Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Fuel Cell Sci. Technol 11(6), 061002 (Sep 16, 2014) (8 pages) Paper No: FC-14-1049; doi: 10.1115/1.4028157 History: Received April 20, 2014; Revised May 26, 2014

A 300 kW solid oxide fuel cell gas turbine (SOFC-GT) power plant simulator is evaluated with the use of a model reference adaptive control (MRAC) scheme, implemented for a set of nonlinear empirical transfer functions. The SOFC-GT simulator allows testing of various fuel cell models under a hardware-in-the-loop configuration that incorporates a 120 kW auxiliary power unit and balance-of-plant components in hardware, and a fuel cell model in software. The adaptation technique is beneficial to plants having a wide range of operation, and strong coupling interaction. The practical implementation of the adaptive methodology is presented through simulation in the Matlab/Simulink environment.

Copyright © 2014 by ASME
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Fig. 1

NETL HyPer test facility

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Fig. 2

CAD rendering of HyPer hardware facility

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Fig. 3

Diagram of the HyPer facility real-time fuel cell model

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Fig. 4

Bode plot of ω/FV and m·/CA

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Fig. 5

Nonlinear interactions of FV and CA on ω and m·

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Fig. 6

Simulink plant model

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Fig. 7

Direct MRAC scheme [13]

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Fig. 8

First order MRAC adaptive laws—state feedback

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Fig. 9

First order MRAC with reference model

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Fig. 11

Simulink plant model and controller

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Fig. 12

PI versus MRAC control, nominal TF

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Fig. 13

PI versus MRAC control, deviation from nominal

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Fig. 14

PI versus MRAC close up

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Fig. 15

Plant gain change response—m· step [7]

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Fig. 16

Plant gain change response—ω step [7]




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