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Research Papers

Multicoordination Control Strategy Performance in Hybrid Power Systems

[+] Author and Article Information
Paolo Pezzini

Simulation Modeling and Decision
Science Program,
Ames Laboratory,
U.S. Department of Energy,
1620 Howe Hall,
Ames, IA 50011
e-mail: ppezzini@ameslab.gov

Kenneth M. Bryden

Simulation Modeling and Decision
Science Program,
Ames Laboratory,
U.S. Department of Energy,
1620 Howe Hall,
Ames, IA 50011

David Tucker

National Energy Technology Laboratory,
U.S. Department of Energy,
3610 Collins Ferry Road,
Morgantown, WV 26507

1Present address: Mechanical Engineering Department, Iowa State University, 1620 Howe Hall, Ames, IA 50011.

Manuscript received June 21, 2017; final manuscript received January 30, 2018; published online April 11, 2018. Assoc. Editor: Robert J. Braun.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. 15(3), 031007 (Apr 11, 2018) (15 pages) Paper No: JEECS-17-1074; doi: 10.1115/1.4039356 History: Received June 21, 2017; Revised January 30, 2018

This paper evaluates a state-space methodology of a multi-input multi-output (MIMO) control strategy using a 2 × 2 tightly coupled scenario applied to a physical gas turbine fuel cell hybrid power system. A centralized MIMO controller was preferred compared to a decentralized control approach because previous simulation studies showed that the coupling effect identified during the simultaneous control of the turbine speed and cathode airflow was better minimized. The MIMO controller was developed using a state-space dynamic model of the system that was derived using first-order transfer functions empirically obtained through experimental tests. The controller performance was evaluated in terms of disturbance rejection through perturbations in the gas turbine operation, and setpoint tracking maneuver through turbine speed and cathode airflow steps. The experimental results illustrate that a multicoordination control strategy was able to mitigate the coupling of each actuator to each output during the simultaneous control of the system, and improved the overall system performance during transient conditions. On the other hand, the controller showed different performance during validation in simulation environment compared to validation in the physical facility, which will require a better dynamic modeling of the system for the implementation of future multivariable control strategies.

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Figures

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Fig. 1

Fuel cell–gas turbine diagram

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Fig. 2

Physical components integrated in the hybrid configuration at the Hyper project

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Fig. 3

Type-one system with integral action and reference input

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Fig. 4

Multi-input multi-output control architecture with communicative effect between decentralized interactions

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Fig. 5

Electric load transfer function versus turbine speed and cathode airflow

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Fig. 6

Cold-air bypass transfer function versus turbine speed and cathode airflow

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Fig. 9

Experiment 2—test 3, cold-air bypass–cathode airflow control loop performance during setpoint tracking maneuver

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Fig. 8

Experiment 1—test 2, electric load–turbine speed control loop performance under disturbance rejection

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Fig. 10

Experiment 3—test 1, turbine speed setpoint tracking maneuver during simultaneous control of actuators

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Fig. 11

Experiment 3—test 2, fuel valve disturbance rejection during simultaneous control of actuators

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Fig. 7

Experiment 1—test 1, turbine speed setpoint tracking maneuver

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Fig. 12

Experiment 3—test 3, cathode airflow setpoint tracking maneuver during simultaneous control of actuators

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