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Learning–Assisted Constraint Filtering To Enhance Power System Optimization Performance, Fouad Hasan
Learning–Assisted Constraint Filtering To Enhance Power System Optimization Performance, Fouad Hasan
LSU Doctoral Dissertations
Machine learning (ML) is a powerful tool that provides meaningful insights for operators to make fast and efficient decisions by analyzing data from power systems. ML techniques have great potential to assist in solving optimization problems within a shorter time frame and with less computational burden. AC optimal power flow (ACOPF), dynamic economic dispatch (D-ED), and security-constrained unit commitment (SCUC) are the three energy management optimization functions studied in this dissertation. ACOPF is solved every 5~15 minutes. Because of the nonconvex and complex nature of ACOPF, solving this problem for large systems is computationally expensive and time-consuming. Classification and regression …
Microcavity Enhanced Beaming And Magneto-Optical Switching Of Light, Ali Haddadpour
Microcavity Enhanced Beaming And Magneto-Optical Switching Of Light, Ali Haddadpour
LSU Doctoral Dissertations
In this dissertation, we show numerically that a compact structure, consisting of multiple optical microcavities at both the entrance and exit sides of a subwavelength plasmonic slit, can lead to greatly enhanced directional transmission through the slit. The microcavities increase the resonant enhancement of the emission in the normal direction and/or the coupling between free space waves and the slit mode. An optimized structure with two microcavities on both the entrance and exit sides of the slit leads to ~ 16 times larger transmission cross section per unit angle in the normal direction compared to the optimized reference slit without …
Design And Optimization Of Nanoplasmonic Waveguide Devices, Pouya Dastmalchi
Design And Optimization Of Nanoplasmonic Waveguide Devices, Pouya Dastmalchi
LSU Doctoral Dissertations
In this dissertation, we introduce compact absorption switches consisting of plasmonic metal-dielectric-metal (MDM) waveguides coupled to multisection cavities. The optimized multisection cavity switches lead to greatly enhanced modulation depth compared to optimized conventional Fabry-Perot cavity switches. We find that the modulation depth of the optimized multisection cavity switches is greatly enhanced compared to the optimized conventional Fabry-Perot cavity switches due to the great enhancement of the total electromagnetic field energy in the cavity region. We then investigate how to improve the computational efficiency of the design of nanoplasmonic devices. More specifically, we show that the space mapping algorithm, originally developed …
Fault Detection Filter Design For Linear Systems, Xiaobo Li
Fault Detection Filter Design For Linear Systems, Xiaobo Li
LSU Doctoral Dissertations
This dissertation considers residual generation for robust fault detection of linear systems with control inputs, unknown disturbances and possible faults. First, multi-objective fault detection problems such as $\mathscr{H_-}/ \mathscr{H_\infty}$, $\mathscr{H}_2/\mathscr{H_\infty}$ and $\mathscr{H_\infty}/\mathscr{H_\infty}$ have been formulated for linear continuous time-varying systems (LCTVS) in time domain for finite horizon and infinite horizon case, respectively. It is shown that under mild assumptions, the optimal solution is an observer determined by solving a standard differential Riccati equation (DRE). The solution is also extended to the case when the initial state for the system is unknown. Second, the parallel problems are also solved for linear …