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Research Papers

Linear Quadratic Regulator for a Bottoming Solid Oxide Fuel Cell Gas Turbine Hybrid System

[+] Author and Article Information
Fabian Mueller

National Fuel Cell Research Center, University of California at Irvine, Irvine, CA 92697fm@nfcrc.uci.edu

Faryar Jabbari

National Fuel Cell Research Center, University of California at Irvine, Irvine, CA 92697fjabbari@uci.edu

Jacob Brouwer1

National Fuel Cell Research Center, University of California at Irvine, Irvine, CA 92697jb@nfcrc.uci.edu

S. Tobias Junker

 FuelCell Energy, Inc., 3 Great Pasture Road, Danbury, CT 06813tjunker@fce.com

Hossein Ghezel-Ayagh

 FuelCell Energy, Inc., 3 Great Pasture Road, Danbury, CT 06813hghezel@fce.com

1

Corresponding author.

J. Dyn. Sys., Meas., Control 131(5), 051002 (Aug 17, 2009) (9 pages) doi:10.1115/1.3155007 History: Received November 20, 2007; Revised April 27, 2009; Published August 17, 2009

The control system for fuel cell gas turbine hybrid power plants plays an important role in achieving synergistic operation of subsystems, improving reliability of operation, and reducing frequency of maintenance and downtime. In this paper, we discuss development of advanced control algorithms for a system composed of an internally reforming solid oxide fuel cell coupled with an indirectly heated Brayton cycle gas turbine. In high temperature fuel cells it is critical to closely maintain fuel cell temperatures and to provide sufficient electrochemical reacting species to ensure system durability. The control objective explored here is focused on maintaining the system power output, temperature constraints, and target fuel utilization, in the presence of ambient temperature and fuel composition perturbations. The present work details the development of a centralized linear quadratic regulator (LQR) including state estimation via Kalman filtering. The controller is augmented by local turbine speed control and integral system power control. Relative gain array analysis has indicated that independent control loops of the hybrid system are coupled at time scales greater than 1 s. The objective of the paper is to quantify the performance of a centralized LQR in rejecting fuel and ambient temperature disturbances compared with a previously developed decentralized controller. Results indicate that both the LQR and decentralized controller can well maintain the system power to the disturbances. However, the LQR ensures better maintenance of the fuel cell stack voltage and temperature that can improve high temperature fuel cell system durability.

FIGURES IN THIS ARTICLE
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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Bottoming SOFC/GT hybrid system with variable speed GT and supplemental oxidizer fuel

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Figure 2

Relative gain array analysis (5) for fuel cell current/system power input/output pairing

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Figure 3

Centralized control system for robust tracking and disturbance rejection

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Figure 6

Nonlinear, linear, and reduced order model open loop response to a 40°C diurnal temperature perturbation. Note that the linear and reduced lines overlap in all the figures and that all the lines overlap in the system power, fuel cell temperature, rpm, and combustor temperature figures.

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Figure 7

Nonlinear, linear, and reduced order model open loop short time response to an instantaneous 5% decrease in the fuel’s methane mole fraction. Note that the linear and reduced lines overlap in all the figures and that all the lines overlap in the rpm and combustor temperature figures.

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Figure 8

Nonlinear, linear, and reduced order model open loop long time response to an instantaneous 5% decrease in the fuel’s methane mole fraction. Note that the linear and reduced lines overlap.

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Figure 9

Controlled system response of the designed LQR applied to the linear model

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Figure 10

Simultaneously simulated 20% instantaneous reduction in the fuel’s methane mole fraction at time 0, and a 40°C diurnal temperature perturbation

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Figure 11

Controlled system long-term (40 h) response to the disturbance of Fig. 1 using the control system with LQR feedback controller and the decentralized feedback controller

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Figure 12

Controlled system short-term (80 s) response to the disturbance of Fig. 1 as controlled by the LQR feedback controller and the decentralized feedback controller

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Figure 4

Selected Bode frequency response of the full state linear model (solid line) and the reduced order linear model (dotted line)

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Figure 5

Simultaneously simulated instantaneous 5% decrease in the fuel’s methane mole fraction at time 0 and a 40°C diurnal temperature fluctuation

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