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Research Papers

Simultaneous Turbine Speed Regulation and Fuel Cell Airflow Tracking of a SOFC/GT Hybrid Plant With the Use of Airflow Bypass Valves

[+] Author and Article Information
Alex Tsai

Assistant Professor  United States Coast Guard Academy Department of Engineering, McAllister Hall, 27 Mohegan Ave., New London, CT 06320alex.tsai@uscga.edu

David Tucker

Research Scientist Department of Energy,  National Energy Technology Laboratory, 3610 Collins Ferry Rd., Morgantown, WV 26505david.tucker@netl.doe.gov

David Clippinger

Assistant Professor  United States Coast Guard Academy Department of Engineering, McAllister Hall, 27 Mohegan Ave., New London, CT 05320david.c.clippinger@uscga.edu

J. Fuel Cell Sci. Technol 8(6), 061018 (Sep 30, 2011) (10 pages) doi:10.1115/1.4004643 History: Received May 24, 2011; Revised June 25, 2011; Published September 30, 2011; Online September 30, 2011

This paper studies a novel control methodology aimed at regulating and tracking turbo machinery synchronous speed and fuel cell mass flow rate of a SOFC/GT hardware simulation facility with the sole use of airflow bypass valves. The hybrid facility under consideration consists of a 120 kW auxiliary power unit gas turbine coupled to a 300 kW SOFC hardware simulator. The hybrid simulator allows testing of a wide variety of fuel cell models under a hardware-in-the-loop configuration. Small changes in fuel cell cathode airflow have shown to have a large impact on system performance. Without simultaneous control of turbine speed via load or auxiliary fuel, fuel cell airflow tracking requires an alternate actuator methodology. The use of bypass valves to control mass flow rate and decouple turbine speed allows for a greater flexibility and feasibility of implementation at the larger scale, where synchronous speeds are required. This work utilizes empirically derived transfer functions (TF) as the system model and applies a fuzzy logic (FL) control algorithm that can be easily incorporated to nonlinear models of direct fired recuperated hybrid plants having similar configurations. This methodology is tested on a SIMULINK/matlab platform for various perturbations of turbine load and fuel cell heat exhaust.

Copyright © 2011 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

NETL Hyper Test Facility

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Figure 2

Diagram of the HyPer facility real-time fuel cell model

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Figure 3

Fuzzy inference system [20]

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Figure 4

Conditions for establishment of rule base sets

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Figure 5

Fuzzy inference process: Mamdani clipping method

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Figure 6

Normalized membership functions for e, de/dt

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Figure 7

BA output membership functions

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Figure 8

CA output membership functions

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Figure 9

Fuzzy rule surface viewer for BA valve output MF

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Figure 10

Fuzzy control system for two inputs

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Figure 11

Combined fuzzy logic implementation

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Figure 12

Decoupled noninteracting control network [6]

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Figure 13

Antiwindup backcalculation scheme

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Figure 14

Comparison: FL and PID CA actuation for a linear operating range: Individual simulation

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Figure 15

Comparison: FL and PID BA actuation for a linear operating range: Individual simulation

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Figure 16

CA fuzzy comparison: Detailed versus nondetailed rules

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Two sets of fuzzy rules: A rules = 9, B rules = 25. The slightest difference in MF definition results in large discrepancies.

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Figure 18

2% FV disturbance and rejection

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Figure 19

+ 10 kW LB disturbance and rejection

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Figure 20

FL control with/without linear FF compensation. BA closes to − 60% beyond constraint.

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Figure 21

Nonlinear system response to a 50% increase in the plant static constant, and 90% decrease in time constant

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Figure 22

Centralized decoupled PID/fuzzy logic scheme

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Figure 23

PID CA controller with AW and FF compensation: FV, LB, BA disturbances on m·

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Figure 24

Transfer function matrix: Rows (m·, ω), columns (CA, BA, FV, LB)

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