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TECHNICAL PAPERS

# Control Design 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, CAfm@nfcrc.uci.edu

Faryar Jabbari

National Fuel Cell Research Center, University of California at Irvine, Irvine, CAfjabbari@uci.edu

Jacob Brouwer

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

Rory Roberts

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

Tobias Junker

FuelCell Energy, Inc., 3 Great Pasture Road, Danbury, CTtjunker@fce.com

Hossein Ghezel-Ayagh

FuelCell Energy, Inc., 3 Great Pasture Road, Danbury, CThghezel@fce.com

In MATLAB , an algebraic loop occurs when a function’s input depends on its own output. To compute the output, MATLAB requires iterations, which significantly slows down the simulation. Also, systems with algebraic loops cannot be linearized using MATLAB ’s LINMOD command.

J. Fuel Cell Sci. Technol 4(3), 221-230 (Dec 19, 2006) (10 pages) doi:10.1115/1.2713785 History: Received October 09, 2006; Revised December 19, 2006

## Abstract

A bottoming $275kW$ planar solid oxide fuel cell (SOFC) gas turbine (GT) hybrid system control approach has been conceptualized and designed. Based on previously published modeling techniques, a dynamic model is developed that captures the physics sufficient for dynamic simulation of all processes that affect the system with time scales of $>10ms$. The dynamic model was used to make system design improvements to enable the system to operate dynamically over a wide range of power output (15–100% power). The wide range of operation was possible by burning supplementary fuel in the combustor and operating the turbine at variable speed for improved thermal management. The dynamic model was employed to design a control strategy for the system. Analyses of the relative gain array (RGA) of the system at several operating points gave insight into input/output (I/O) pairing for decentralized control. Particularly, the analyses indicate that, for SOFC/GT hybrid plants that use voltage as a controlled variable, it is beneficial to control system power by manipulating fuel cell current and to control fuel cell voltage by manipulating the anode fuel flowrate. To control the stack temperature during transient load changes, a cascade control structure is employed in which a fast inner loop that maintains the GT shaft speed receives its set point from a slower outer loop that maintains the stack temperature. Fuel can be added to the combustor to maintain the turbine inlet temperature for the lower operating power conditions. To maintain fuel utilization and to prevent fuel starvation in the fuel cell, fuel is supplied to the fuel cell proportionally to the stack current. In addition, voltage is used as an indicator of varying fuel concentrations, allowing the fuel flow to be adjusted accordingly. Using voltage as a sensor is shown to be a potential solution to making SOFC systems robust to varying fuel compositions. The simulation tool proved effective for fuel cell/GT hybrid system control system development. The resulting SOFC/GT system control approach is shown to have transient load-following capability over a wide range of power, ambient temperature, and fuel concentration variations.

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## Figures

Figure 1

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

Figure 2

Compressor flow map with operating points

Figure 3

Figure 4

Relative gain array analysis for fuel cell current-system power input and output pairing

Figure 5

Relative gain array analysis for fuel cell current-fuel cell voltage input and output pairing

Figure 6

System power controller

Figure 7

System response to an instantaneous power demand increase from 70kW to 250kW

Figure 8

Figure 9

Combustor temperature controller

Figure 10

System response to ambient temperature variation from 5°C to 35°C

Figure 11

Anode fuel flow controller

Figure 12

System response to an instantaneous 40% decrease in fuel methane mole fraction

Figure 13

Simulated power demand, ambient temperature, and fuel methane mole fraction

Figure 14

Simulated system response conditions presented in Fig. 1

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