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Control Design And Simulation Research Of Stand-Alone Solar Fuel Cell Hybrid Generation System

Posted on:2008-02-04Degree:DoctorType:Dissertation
Country:ChinaCandidate:W LiFull Text:PDF
GTID:1102360242476033Subject:Control theory and control engineering
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Currently, the pollution of the environment and energy's lacking tightly is the two greatest problems to cracks that mankind's existence. Scientists think that those conventional fossil-fuel energy sources, such as oil and coal, will diminish in fifty years. Therefore, renewable energy sources (solar, wind, tidal and geothermal, etc.) are attracting more attention as alternative energy sources. Solar fuel cell hybrid generation system emerges as the times require. The hybrid generation mode to combine solar with fuel cell is one of the most effective methods to make use of renewable energy sources. It is also one of focus that current energy science technique foundation studies an international competition. So the hybrid generation technology is once put forward, all kinds of power research institutes, higher school, the power company and the factory begin to research the hybrid generation technology. At the same time, developed experiment equipment to apply to real power system as the demonstration project.The solar fuel cell hybrid generation is actually an energy transport chain as solar - hydrogen - fuel cell - electric power– user. There, the fuel cell plays a pivotal role. The hybrid energy management system has characteristics of multi-input multi-output, complex structure, many parameters. Thus, make use of each advantage of solar and fuel cell and according to some experiences on hybrid generation system in China and foreign countries and for real applications, the dissertation design a stand-alone solar fuel cell hybrid generation system to meet user's electric requirements. This is not only one of the design difficulty, but also the focus. Start with the technique problem before the solar fuel cell hybrid generation system design, firstly established a complete and effective model; secondly carried on the control design and simulation analysis; lastly researched the coordination and control strategy for the total solar fuel cell hybrid system. The hybrid generation system has good performance by simulation analysis and it can operate steadily, providing valuable references for developments and applications of real hybrid generation system.The main contributions in the thesis are as follows:Solar fuel cell hybrid generation system has many advantages. It can provide energy unremittingly, lower energy depletion, carry out to make use of energy sources effectively and environmental clean etc. However, the hybrid generation system stands in the initial research stage, which is far away from the foreign level. With little experience, the design of system has many problems to solve. So, the operation experiences and running test results are used for reference to provide foundation and guarantee for this paper. Before design the hybrid system, there are many technique problems to confirm. These technique problems include: design principle, component unit, size matching, energy management, the connection method between PV array and electrolyzer, maximum power point tracking (MPPT), load characteristics and power converter unit (PCU) etc. these are key factors of electric efficiency and performance and foundations to design the hybrid generation. So, these technique problems firstly are analyzed and evaluated.On the basis of the existing references, according to the structure characteristic of solar fuel cell hybrid generation system in the paper, the photovoltaic cell, electrolyzer, PEMFC stack, gas storage and power converter unit are modeled separately in MATLAB/SIMULINK simulation environment. For the heavy structure and complex performances of the hybrid system, based on the photovoltaic effect, the state equation of ideal gas, mass and energy conservation equations, physicochemical principles, power transformation relations, flow and transmission of the heat energy, first give a set of proper assumptions and simplifications, then a set of dynamic model of all subsystem are established completely and detailed. Simulation analysis show that the model is able to and also enough to reflect all operating parameters, power output and heat recovery very well, so, it can be used in the control design, performance analysis and optimization of the system.A detailed control design strategy was analyzed based on the dynamic model and simulation tests. After system design, analysis and evaluation, adopted BP neural network PID control method to track the maximum power point. The key control designs of fuel cell are the PEMFC stack temperature, pressure and voltage. For the one to one relationship of between input and output, single neuron PID controller was used. It is not only to realize the control, but also to regulate the parameters on-line. The same is true of electrolyzer, single neuron PID controller was used to control the operation parameters of electrolyzer.The coordination and control design for the real running of complex system are very important. It is not only the premise of system application, but also the effective method to improve the system performance. Hybrid generation system requires the security, reliability and durability. In order to satisfy these requirements, the main operation parameters of performance influence, such as temperature, pressure, voltage, current, are kept in the ideal steady state. It is because that when the load varies, the operation parameters will deviate from the normal operation scope, even to damage the system. At the same time, because the hybrid generation system has many units, many control variables and complex energy management, a complete coordination control design was carried out to satisfy the user electric requirement.
Keywords/Search Tags:Solar, Proton exchange membrane fuel cell (PEMFC), Hydrogen, Electrolyzer, Power converter unit (PCU), Hybrid generation system, Maximum power point tracking, Energy management system (EMS), BP neural network PID control
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