Study Of Modeling And Control For Molten Carbonate Fuel Cell/Micro Gas Turbine Combined Generation System Based On Intelligent Strategy

Fuel cells have become one of the most important generation technologies. It can convert chemical energy directly to electricity without the limitation of carnot cycle; therefore, the efficiency of fuel cell can reach a high level. In general, according the operating temperature, fuel cells are classified as"high-temperature fuel cell"and"low-temperature fuel cell". Due to the high potential energy from the exhaust of high-temperature fuel cell, combination with a micro gas turbine to form a combined system is attractive. The efficiency of"combined system"can exceed the sum of the two equipments, therefore this method is considered as the optimum distributed generation mode.At present, the main researches on high-temperature fuel cell / micro gas turbine combined generation system are concentrated in the following two aspects in domestic and foreign: one is the researches and developments on new material of fuel cells stack, researches and developments on micro gas turbine adapted to combined generation system and on the optimization and improving the structure of combined generation system and assemble techniques; the other is the establishments of mathematical models according to the interior mechanism of combined generation system, and study the effects of system operating parameters on performance of combined system based on these models. However, there are only a few researches on control strategy for combined generation system. In order to guarantee stability of combined system during the process of operation and improve the generation performance, solving the control problems of combined system becomes extremely urgent.The task is part of the national 863 scientific project item"Analysis and control strategy for 10 kW-scale molten carbonate fuel cells power generation system"and the national natural scientific project item"Harmonious nonlinear control of fuel cell-gas turbine hybrid generation system"being researched in the institute of fuel cell of Shanghai Jiaotong University. Firstly, according the reaction mechanism, the mechanism models of a micro gas turbine and a MCFC stack are established, and the effects of operating parameters on the combined system are analyzed; Secondly, three kinds of intelligent nonlinear controllers for controlling the important temperature parameters of combined system are designed, including fuzzy controller, Elman neural network auto-adjust controller and fuzzy neural network controller. The comparisons of control performance among these controllers are realized in simulation tests. Finally, two kinds of modeling approaches are presented to model the output power of the combined system: radial basis function neural network and least squares support vector machine. Based on these two models, fuzzy neural network controller and a novel nonlinear predictive controller based on improved genetic algorithm are proposed to guarantee the optimum operation of MCFC/MGT combined system, comparisons between the two controllers are given and the results are analyzed. The main contributions and achievements of this thesis are given below:1,Establish the dynamic model of a micro gas turbine. According to the operating mechanism of micro gas turbine and using the law of mass conservation, energy conservation, ideal gas laws and thermodynamics formula, the components models of a micro gas turbine are built in MATLAB/SIMULINK environment. Based on this model, the static and dynamic performances of the micro gas turbine are analyzed.2,Build up the dynamic model of a molten carbonate fuel cell stack. The reaction mechanism is investigated deeply and using the conservation law of mass and energy, and ideal gas law, the dynamic model of a MCFC is built. Whole combination system model are made up of the MCFC model and the micro gas turbine model. Based on the combination system model, the effects of some important operating parameters on the whole system are investigated and evaluated for optimization and control of the whole system.3,According to the analysis results, the control strategy for the whole combined system is designed, and then, three kinds of controllers are presented to control the MCFC operating temperature and turbine inlet temperature parameter, including traditional fuzzy controller, Elman neural network auto-adjust controller and fuzzy neural network controller. Many simulations tests are implemented to evaluate the control performance of the three controllers, test results reveal that the fuzzy neural network controller can provide the best control performance. Finally, the reasons are analyzed.4,Design the control strategy for controlling output power of the combined system. Unlike the mechanism model, two kinds of data models based on the input-output data of controlled object are established. One is the RBF neural network model, the other is the least squares support vector machine model. Simulation results reveal that the LS-SVM model possesses better identification performance. Based on these two models, fuzzy neural network controller and a novel nonlinear predictive controller based on improved genetic algorithm are proposed. These two controllers are compared with each other in simulation tests. The simulation results show the effectiveness and merits of both the controllers. However, nonlinear predictive controller has advantage in the control performance.