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Full-Text Articles in Engineering
Multigrid For The Nonlinear Power Flow Equations, Enrique Pereira Batista
Multigrid For The Nonlinear Power Flow Equations, Enrique Pereira Batista
Mathematics Theses and Dissertations
The continuously changing structure of power systems and the inclusion of renewable
energy sources are leading to changes in the dynamics of modern power grid,
which have brought renewed attention to the solution of the AC power flow equations.
In particular, development of fast and robust solvers for the power flow problem
continues to be actively investigated. A novel multigrid technique for coarse-graining
dynamic power grid models has been developed recently. This technique uses an
algebraic multigrid (AMG) coarsening strategy applied to the weighted
graph Laplacian that arises from the power network's topology for the construction
of coarse-grain approximations to …
On-Chip Nonreciprocal Components For Full-Duplex Communications And Gaussian Regulated Gate Driver For Electromagnetic Interference Reduction, Chang Yang
Electrical Engineering Theses and Dissertations
This dissertation is comprised of two unrelated design endeavors. The first one is about two CMOS nonreciprocal components: 1) an isolator and 2) a circulator. To make the components compact enough for the next generation communication systems with wide bandwidth, both components operate at 100 GHz band for full-duplex transceivers for ultra-high-data-rate millimeter-wave wireless communication. The proposed nonreciprocal structures are based on a time-domain modulation by signals at around 1/6 of the RF frequencies and spatial duplexing over the RF signal paths, demonstrating over 45 dB isolation in a bandwidth of 1.5 GHz over the tuning range of 85-110 GHz. …
Model-Based And Data-Driven Situational Awareness For Distribution System Monitoring And Control, Ying Zhang
Model-Based And Data-Driven Situational Awareness For Distribution System Monitoring And Control, Ying Zhang
Electrical Engineering Theses and Dissertations
Electric power systems are undergoing a dramatic change. The penetration of distributed energy resources (DERs) such as wind turbine generators and photovoltaic panels is turning a traditional power system into the active distribution network. Power system situational awareness, which provides critical information for system monitoring and control, is being challenged by multiple sources of uncertainties such as random meter errors, stochastic power output of DERs, and imprecise network parameters. On the other hand, cyber-physical power system operation is vulnerable to cyberattacks against effective state estimation, such as false data injection attacks (FDIAs). To construct next-generation smart grids, this dissertation develops …
Learning Deep Architectures For Power Systems Operation And Analysis, Mahdi Khodayar
Learning Deep Architectures For Power Systems Operation And Analysis, Mahdi Khodayar
Electrical Engineering Theses and Dissertations
With the rapid increase in size and computational complexities of power systems, the need for powerful computational models to capture strong patterns from energy datasets is emerged. In this thesis, we provide a comprehensive review on recent advances in deep neural architectures that lead to significant improvements in classification and regression problems in the area of power engineering. Furthermore, we introduce our novel deep learning methodologies proposed for a large variety of applications in this area. First, we present the interval deep probabilistic modeling for wind speed forecasting. Incorporating the Rough Set Theory into deep neural networks, we create an …
Machine Learning Applications In Power Systems, Xinan Wang
Machine Learning Applications In Power Systems, Xinan Wang
Electrical Engineering Theses and Dissertations
Machine learning (ML) applications have seen tremendous adoption in power system research and applications. For instance, supervised/unsupervised learning-based load forecasting and fault detection are classic ML topics that have been well studied. Recently, reinforcement learning-based voltage control, distribution analysis, etc., are also gaining popularity. Compared to conventional mathematical methods, ML methods have the following advantages: (i). better robustness against different system configurations due to its data-driven nature; (ii). better adaption to system uncertainties; (iii). less dependent on the modeling accuracy and validity of assumptions. However, due to the unique physics of the power grid, many problems cannot be directly solved …