Fabrication and energetic characterization of micro and nano sized Al/CuO core-shell particles

Thermite is one type of energetic material which could release a large amount of heat through an oxidation-reduction reaction. Thermite material has a lot of application in joining, metal refining and propulsion. Al/CuO is a well-studied thermite system enabling high energy release and production of pure copper from the reaction. Common Al/CuO thermites include composites of Al and CuO micro- and nano-particles, multi-layered Al and CuO structures, and agglomeration of particles. There is a gap in fabrication and characterization of individual and spherical Al/CuO core-shell particles that have promising application in mobile delivery of energy. A new synthesis method for producing spherical Al/CuO particles with a core-shell structure is introduced in this thesis. The compositions and microstructures of as-produced samples are investigated during fabrication and over the thermite reaction through which the core-shell geometry is characterized and the reaction mechanism is studied

Research on Power System State Estimation Problems – Series-Compensated Transmission Line Parameter and Load Model Parameter Estimation

Transmission line and load model parameters are essential inputs to power system modeling and simulation, control, protection, operation, optimization, and planning. These parameters usually vary over time or under different operating conditions. Thus, reliable estimation methods are desired to ensure the accuracy of those parameters. This research focuses on estimation for transmission line parameters and the ZIP load model. The proposed estimation methods can use both online measurements and historical data of a specified duration. The parameters of long transmission lines with different series-compensation configurations are estimated using linear methods and optimal estimators with bad data detection capability. Additionally, Kalman filter estimation methods have been proposed to improve the estimation accuracy and to track the dynamically changing line parameters under the effect of measurement noises. The estimation methods are tested with data generated using Matlab Simulink. For the ZIP load model parameter estimation, theoretical formulation for the aggregate ZIP load model has been established. The least squares, optimization, neural network, and Kalman filter methods have been investigated to estimate ZIP parameters and been verified based on OpenDSS simulation data