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Research Papers

Multiple Model Adaptive Estimation of a Hybrid Solid Oxide Fuel Cell Gas Turbine Power Plant Simulator

[+] Author and Article Information
Alex Tsai

United States Coast Guard Academy,
New London, CT 06320

David Tucker

United States Department of Energy,
National Energy Technology Laboratory,
Morgantown, WV 26507

Tooran Emami

United States Coast Guard Academy,
New London CT 06320

Manuscript received May 11, 2017; final manuscript received September 20, 2017; published online April 2, 2018. Assoc. Editor: Robert J. Braun.This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.

J. Electrochem. En. Conv. Stor. 15(3), 031004 (Apr 02, 2018) (12 pages) Paper No: JEECS-17-1046; doi: 10.1115/1.4038634 History: Received May 11, 2017; Revised September 20, 2017

Operating points of a 300 kW solid oxide fuel cell gas turbine (SOFC-GT) power plant simulator are estimated with the use of a multiple model adaptive estimation (MMAE) algorithm. This algorithm aims to improve the flexibility of controlling the system to changing operating conditions. Through a set of empirical transfer functions (TFs) derived at two distinct operating points of a wide operating envelope, the method demonstrates the efficacy of estimating online the probability that the system behaves according to a predetermined dynamic model. By identifying which model the plant is operating under, appropriate control strategies can be switched and implemented. These strategies come into effect upon changes in critical parameters of the SOFC-GT system—most notably, the load bank (LB) disturbance and fuel cell (FC) cathode airflow parameters. The SOFC-GT simulator allows the testing of various FC models under a cyber-physical configuration that incorporates a 120 kW auxiliary power unit and balance-of-plant (Bop) components. These components exist in hardware, whereas the FC model in software. The adaptation technique is beneficial to plants having a wide range of operation, as is the case for SOFC-GT systems. The practical implementation of the adaptive methodology is presented through simulation in the matlab/simulink environment.

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References

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Figures

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Fig. 1

National Energy Technology Laboratory HyPer test facility

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Fig. 2

Computer-aided design rendering of HyPer hardware facility

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Fig. 3

Fuel cell m˙ response: CA 20–40% step

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Fig. 4

Open loop turbine speed response: CA 20–40% step

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Fig. 5

Fuel cell m˙ response: CA 40–80% step

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Fig. 6

Open loop turbine speed response: CA 40–80% step

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Fig. 7

Open loop turbine speed response: EL 50 kW-30 kW step

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Fig. 8

Fuel cell m˙ response: EL 50 kW-30 kW step

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Fig. 9

Transfer function matrix model: first operating point, Eq. (4)

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Fig. 10

Operating envelope for ω = f(CA, LB), 50 kW load

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Fig. 11

Operating envelope for m˙ = f(CA, LB)

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Fig. 12

Prediction type KF

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Fig. 13

Cold air and LB rich input signals: 0 = nominal value

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Fig. 14

Multiple model adaptive estimation input signals: 0 = nominal value

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Fig. 15

Model probabilities for Q1, R1: first sequence

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Fig. 16

Model residuals for Q1, R1: first sequence

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Fig. 17

Model probabilities for Q1, R1: second sequence

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Fig. 18

Model residuals for Q1, R1: second sequence

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Fig. 19

Model probabilities for QΔ, R1

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Fig. 20

Model residuals for QΔ, R1

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Fig. 21

Model probabilities for RΔ, Q1

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Fig. 22

Model residuals for RΔ, Q1

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