Multivariable robust control of a simulated hybrid solid oxide fuel cell gas turbine plant

Abstract

This work presents a systematic approach to the multivariable robust control of a hybrid fuel cell gas turbine plant. The hybrid configuration under investigation built by the National Energy Technology Laboratory comprises a physical simulation of a 300kW fuel cell coupled to a 120kW auxiliary power unit single spool gas turbine. The public facility provides for the testing and simulation of different fuel cell models that in turn help identify the key difficulties encountered in the transient operation of such systems. An empirical model of the built facility comprising a simulated fuel cell cathode volume and balance of plant components is derived via frequency response data. Through the modulation of various airflow bypass valves within the hybrid configuration, Bode plots are used to derive key input/output interactions in transfer function format. A multivariate system is then built from individual transfer functions, creating a matrix that serves as the nominal plant in an H ∞ robust control algorithm. The controller's main objective is to track and maintain hybrid operational constraints in the fuel cell's cathode airflow, and the turbo machinery states of temperature and speed, under transient disturbances. This algorithm is then tested on a Simulink/MatLab platform for various perturbations of load and fuel cell heat effluence. As a complementary tool to the aforementioned empirical plant, a nonlinear analytical model faithful to the existing process and instrumentation arrangement is evaluated and designed in the Simulink environment. This parallel task intends to serve as a building block to scalable hybrid configurations that might require a more detailed nonlinear representation for a wide variety of controller schemes and hardware implementations. iii Pray, hope, and don't worry. Do not worry over things that generate anxiety. Only one thing is necessary, to lift up your spirit and love God.

Experimental characterization of fuel cell gas turbine power system and determination of optimal trajectories of operation using a model predictive controller

“…The system was composed of a GT cycle coupled to an atmospheric FC. Tsai et al [26], [27], [28], and [29] disturbances, and optimize operation.…”

“…This undesired phenomenon is called the -windup effect". The -wind up‖ situation is present in this system, and an anti-windup design must be included in the controller in order to maintain control of the plant (Tsai, [26]). The MPC is formulated based on an optimization mathematical framework which allows a very straight forward formulation of the input and output constraints (Arora [52], Venkataraman [53], and Elster [54]).…”

Biomimetic and Advanced Control Structure Design with Real Time Optimization

“…Pressure vessels(Air plenum, Combustor, Post-combustor) A description of each subsystem is given in section 4.3.1 through 4.3.4. For brevity, all model equations for the subsystems are omitted here and can be found in (Tsai et al, 2010).…”

“…Here is the number of shift registers and is the switching time. The PRBS sequence is The plant used in the system identification is obtained from a first principles model developed by (Tsai et al, 2010). To identify linear time invariant models, two distinct simulations were run.…”

“…These are outlined in Table 2.13. The nominal values of the steady state operating point was obtained from (Tsai et al, 2010) as 13.5g/s~39% ± 10%, 45kW±5kW, 14±4%, 40±10%, 40±10% for the fuel valve, load bank, bleed air, cold air and hot air bypass respectively. The gain matrices are obtained using the data obtained from the simulation of the available SIMULINK file.…”