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

2018 ◽  
Vol 15 (3) ◽  
Author(s):  
Alex Tsai ◽  
David Tucker ◽  
Tooran Emami
Keyword(s):  
Fuel Cell ◽  
Power Plant ◽  
Solid Oxide Fuel Cell ◽  
Gas Turbine ◽  
Adaptive Estimation ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Multiple Model ◽  
Multiple Model Adaptive Estimation ◽  
Operating Points

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.

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

2016 ◽  
Author(s):  
Alex Tsai ◽  
David Tucker ◽  
Tooran Emami
Keyword(s):  
Fuel Cell ◽  
Power Plant ◽  
Solid Oxide Fuel Cell ◽  
Gas Turbine ◽  
Adaptive Estimation ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Multiple Model ◽  
Multiple Model Adaptive Estimation ◽  
Operating Points

Operating points of a 300kW Solid Oxide Fuel Cell Gas Turbine (SOFC-GT) power plant simulator is estimated with the use of a Multiple Model Adaptive Estimation (MMAE) algorithm, aimed at improving the flexibility of controlling the system to changing operating conditions. Through a set of empirical Transfer Functions 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 upon changes in critical parameters of the SOFC-GT system — most notably the Load Bank (LB) disturbance and FC cathode airflow parameters. The SOFC-GT simulator allows testing of various fuel cell models under a cyber-physical configuration that incorporates a 120kW Auxiliary Power Unit, and Balance-of-Plant components in hardware, and a fuel cell 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.

System Configuration and Performance Studies of Solid Oxide Fuel Cell -Gas Turbine Hybrid Cycle

2007 ◽  
Author(s):  
Wei Jiang ◽  
Ruxian Fang ◽  
Jamil A. Khan ◽  
Roger A. Dougal
Keyword(s):  
Fuel Cell ◽  
Power Plant ◽  
Solid Oxide Fuel Cell ◽  
Gas Turbine ◽  
Finite Volume ◽  
Hybrid System ◽  
High Energy ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Hybrid Cycle

Fuel Cell is widely regarded as a potential alternative in the electric utility due to its distinct advantages of high energy conversion efficiency, low environmental impact and flexible uses of fuel types. In this paper we demonstrate the enhancement of thermal efficiency and power density of the power plant system by incorporating a hybrid cycle of Solid Oxide Fuel Cell (SOFC) and gas turbine with appropriate configurations. In this paper, a hybrid system composed of SOFC, gas turbine, compressor and high temperature heat exchanger is developed and simulated in the Virtual Test Bed (VTB) computational environment. The one-dimensional tubular SOFC model is based on the electrochemical and thermal modeling, accounting for the voltage losses and temperature dynamics. The single cell is discretized using a finite volume method where all the governing equations are solved for each finite volume. Simulation results show that the SOFC-GT hybrid system could achieve a 70% total electrical efficiency (LHV) and an electrical power output of 853KW, around 30% of which is produced by the power turbine. Two conventional power plant systems, i.e. gas turbine recuperative cycle and pure Fuel Cell power cycle, are also simulated for the performance comparison to validate the improved performance of Fuel Cell/Gas Turbine hybrid system. Finally, the dynamic behavior of the hybrid system is presented and analyzed based on the system simulation.

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

2017 ◽  
Author(s):  
Alex Tsai ◽  
Paolo Pezzini ◽  
David Tucker ◽  
Kenneth M. Bryden
Keyword(s):  
Adaptive Control ◽  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Gas Turbine ◽  
Transfer Functions ◽  
Solid Oxide ◽  
Critical Parameters ◽  
Oxide Fuel ◽  
Multiple Model ◽  
Multiple Model Adaptive Control

A Multiple Model Adaptive Control (MMAC) methodology is used to control the critical parameters of a Solid Oxide Fuel Cell Gas Turbine (SOFC-GT) cyberphysical simulator, capable of characterizing 300kW hybrid plants. The SOFC system is comprised of a hardware Balance of Plant (BoP) component, and a high fidelity FC model implemented in software. This study utilizes empirically derived Transfer Functions (TF) of the BoP facility to derive the multi model adaptive controller (MMAC) gains for the BoP system, based on an estimation algorithm which identifies current operating points. The MMAC technique is useful for systems having a wide operating envelope with nonlinear dynamics. The practical implementation of the adaptive methodology is presented through simulation in the MATLAB/SIMULINK environment.

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