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Full-Text Articles in Engineering
Development Of Metaheuristic Algorithms For The Efficient Allocation Of Power Flow Control Devices, Eduardo Jose Castillo Fatule
Development Of Metaheuristic Algorithms For The Efficient Allocation Of Power Flow Control Devices, Eduardo Jose Castillo Fatule
Open Access Theses & Dissertations
Modern energy grids have become extremely complex systems, requiring more variable and active flow control. As a remedy to this, Distributed Flexible AC Transmission Systems (D-FACTS) are cost-efficient devices used to mitigate power flow congestion and integrate renewable energies. The objective of this research is then to propose an efficient multiple objective evolutionary algorithm to solve a stochastic model for D-FACTS allocation, which aims to optimize various objectives related to cost, grid health, and environmental impacts. The model was implemented on a modified RTS-96 test system, and the results show that optimally allocating D-FACTS modules using the proposed model can …
Continuous-Time And Complex Growth Transforms For Analog Computing And Optimization, Oindrila Chatterjee
Continuous-Time And Complex Growth Transforms For Analog Computing And Optimization, Oindrila Chatterjee
McKelvey School of Engineering Theses & Dissertations
Analog computing is a promising and practical candidate for solving complex computational problems involving algebraic and differential equations. At the fundamental level, an analog computing framework can be viewed as a dynamical system that evolves following fundamental physical principles, like energy minimization, to solve a computing task. Additionally, conservation laws, such as conservation of charge, energy, or mass, provide a natural way to couple and constrain spatially separated variables. Taking a cue from these observations, in this dissertation, I have explored a novel dynamical system-based computing framework that exploits naturally occurring analog conservation constraints to solve a variety of optimization …
Machine Learning Morphisms: A Framework For Designing And Analyzing Machine Learning Work Ows, Applied To Separability, Error Bounds, And 30-Day Hospital Readmissions, Eric Zenon Cawi
McKelvey School of Engineering Theses & Dissertations
A machine learning workflow is the sequence of tasks necessary to implement a machine learning application, including data collection, preprocessing, feature engineering, exploratory analysis, and model training/selection. In this dissertation we propose the Machine Learning Morphism (MLM) as a mathematical framework to describe the tasks in a workflow. The MLM is a tuple consisting of: Input Space, Output Space, Learning Morphism, Parameter Prior, Empirical Risk Function. This contains the information necessary to learn the parameters of the learning morphism, which represents a workflow task. In chapter 1, we give a short review of typical tasks present in a workflow, as …
Voltage Security Optimization For Power Transmission Systems, Tamer Ibrahim
Voltage Security Optimization For Power Transmission Systems, Tamer Ibrahim
Dissertations and Theses
This project proposes an optimization approach for day-ahead reactive power planning to ensure voltage security in transmission networks. The problem is formulated as a voltage-secure multi-period optimal reactive power dispatch (MP-ORPD) problem. The optimization approach searches for optimal set-points of dynamic and static reactive power (var) resources. Specifically, the output includes set-points for switching shunts, transformer taps, and voltage magnitudes at the regulated buses. The primary goal is to maximize the dynamic reactive power reserve of the system, by minimizing the reactive power supplied by synchronous generators. The secondary goal is to minimize changes in the settings of switching shunts …
Optimization Of Energy-Constrained Resources In Radial Distribution Networks With Solar Pv, Mohammad Nawaf Nazir
Optimization Of Energy-Constrained Resources In Radial Distribution Networks With Solar Pv, Mohammad Nawaf Nazir
Graduate College Dissertations and Theses
The research objective of the proposed dissertation is to make best use of available distributed energy resources to meet dynamic market opportunities while accounting for AC physics of unbalanced distribution networks and the uncertainty of distributed solar photovoltaics (PV). With ever increasing levels of renewable generation, distribution system operations must shift from a mindset of static unidirectional power flows to dynamic, unpredictable bidirectional flows. To manage this variability, distributed energy resources (DERs; e.g.,solar PV inverters, inverter-based batteries, electric vehicles, water heaters, A/Cs) need to be coordinated for reliable and resilient operation. This introduces the challenge of coordinating such resources at …