Use of active power filter to reduce the effects of harmonics in solid oxide fuel cells

The Solid Oxide Fuel Cell (SOFC) is a class of fuel cells that is capable of generating very high levels of power at high temperatures. SOFCs are used for stationary power generation and as Combined Heat and Power (CHP) systems. In spite of all the beneficial features of the SOFC, the propagation of ripple currents, due to nonlinear loads, is a challenging problem, as it interferes with the physical operation of the fuel cell. The purpose of this thesis is to identify the cause of ripples and attempt to eliminate or reduce the ripple propagation through the use of Active Power Filters (APF). To this end, a systematic approach to modeling the fuel cell to account for its nonlinear behavior in the presence of current ripples is presented. A model of a small fuel cell power system which consists of a fuel cell, a DC-DC converter, a single-phase inverter and a nonlinear load is developed in MATLAB/Simulink environment. The extent of ripple propagation, due to variations in load magnitude and frequency, are identified using frequency spectrum analysis. In order to reduce the effects of ripple propagation, an APF is modeled to remove ripples from the DC fuel cell current. The emphasis of this thesis is based on the idea that small fuel cell systems cannot implement large passive filters to cancel the effects of ripple propagation and hence, the compact APF topology effectively protects the fuel cell from propagating ripples and improves its electrical performance.

Integrated sensitivity analysis, calibration, and uncertainty propagation analysis approaches for supporting hydrological modeling

The successful performance of a hydrological model is usually challenged by the quality of the sensitivity analysis, calibration and uncertainty analysis carried out in the modeling exercise and subsequent simulation results. This is especially important under changing climatic conditions where there are more uncertainties associated with climate models and downscaling processes that increase the complexities of the hydrological modeling system. In response to these challenges and to improve the performance of the hydrological models under changing climatic conditions, this research proposed five new methods for supporting hydrological modeling. First, a design of experiment aided sensitivity analysis and parameterization (DOE-SAP) method was proposed to investigate the significant parameters and provide more reliable sensitivity analysis for improving parameterization during hydrological modeling. The better calibration results along with the advanced sensitivity analysis for significant parameters and their interactions were achieved in the case study. Second, a comprehensive uncertainty evaluation scheme was developed to evaluate three uncertainty analysis methods, the sequential uncertainty fitting version 2 (SUFI-2), generalized likelihood uncertainty estimation (GLUE) and Parameter solution (ParaSol) methods. The results showed that the SUFI-2 performed better than the other two methods based on calibration and uncertainty analysis results. The proposed evaluation scheme demonstrated that it is capable of selecting the most suitable uncertainty method for case studies. Third, a novel sequential multi-criteria based calibration and uncertainty analysis (SMC-CUA) method was proposed to improve the efficiency of calibration and uncertainty analysis and control the phenomenon of equifinality. The results showed that the SMC-CUA method was able to provide better uncertainty analysis results with high computational efficiency compared to the SUFI-2 and GLUE methods and control parameter uncertainty and the equifinality effect without sacrificing simulation performance. Fourth, an innovative response based statistical evaluation method (RESEM) was proposed for estimating the uncertainty propagated effects and providing long-term prediction for hydrological responses under changing climatic conditions. By using RESEM, the uncertainty propagated from statistical downscaling to hydrological modeling can be evaluated. Fifth, an integrated simulation-based evaluation system for uncertainty propagation analysis (ISES-UPA) was proposed for investigating the effects and contributions of different uncertainty components to the total propagated uncertainty from statistical downscaling. Using ISES-UPA, the uncertainty from statistical downscaling, uncertainty from hydrological modeling, and the total uncertainty from two uncertainty sources can be compared and quantified. The feasibility of all the methods has been tested using hypothetical and real-world case studies. The proposed methods can also be integrated as a hydrological modeling system to better support hydrological studies under changing climatic conditions. The results from the proposed integrated hydrological modeling system can be used as scientific references for decision makers to reduce the potential risk of damages caused by extreme events for long-term water resource management and planning.