Analysis and implementation of model predictive control technique for converter control in grid integration with battery energy storage system and fuel cell power system

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2018-08
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Abstract

The focus of this dissertation is to provide an in-depth understanding of the Model predictive control (MPC) technique of a three-phase voltage source converter for use in a fuel cell power system and a battery energy storage system (BESS) in grid-connected applications. The work presented here will help the researchers to further explore the flexibility of the MPC controller for design, analysis, and implementation in a power conversion system. With the growing demand for electricity, renewable sources of energy have garnered a lot of support from all quarters. The problem with these renewable sources is that the output from them is independent of the demand. The storage of electricity gives us an opportunity to effectively manage and balance the supply and demand of electricity. The second part of the dissertation studies the model predictive control technique to integrate a BESS with the grid. As an energy source, fuel cell power systems offer clean and efficient energy production and are currently under intensive development by several manufacturers. The viability, efficiency, and robustness of this technology depend on understanding, predicting and controlling the unique transient behavior of the fuel cell system. In the third part of the dissertation, the mathematical model of a fuel cell power system is designed and the system’s dynamic behavior at a grid-connected mode is discussed. The model predictive control technique is used to integrate the fuel cell power system with the grid. Consequently, this thesis focuses on a novel controller approach in order to obtain a more reliable and flexible three-phase inverter to integrate distributed energy sources with the grid.

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Keywords
Model predictive control, Inverter sontrol, Battery energy storage system
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