Browsing by Author "Nielsen, Aaron, committee member"

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Open Access
Application of statistical and deep learning methods to power grids
(Colorado State University. Libraries, 2023) Rimkus, Mantautas, author; Kokoszka, Piotr, advisor; Wang, Haonan, advisor; Nielsen, Aaron, committee member; Cooley, Dan, committee member; Chen, Haonan, committee member
The structure of power flows in transmission grids is evolving and is likely to change significantly in the coming years due to the rapid growth of renewable energy generation that introduces randomness and bidirectional power flows. Another transformative aspect is the increasing penetration of various smart-meter technologies. Inexpensive measurement devices can be placed at practically any component of the grid. As a result, traditional fault detection methods may no longer be sufficient. Consequently, there is a growing interest in developing new methods to detect power grid faults. Using model data, we first propose a two-stage procedure for detecting a fault in a regional power grid. In the first stage, a fault is detected in real time. In the second stage, the faulted line is identified with a negligible delay. The approach uses only the voltage modulus measured at buses (nodes of the grid) as the input. Our method does not require prior knowledge of the fault type. We further explore fault detection based on high-frequency data streams that are becoming available in modern power grids. Our approach can be treated as an online (sequential) change point monitoring methodology. However, due to the mostly unexplored and very nonstandard structure of high-frequency power grid streaming data, substantial new statistical development is required to make this methodology practically applicable. The work includes development of scalar detectors based on multichannel data streams, determination of data-driven alarm thresholds and investigation of the performance and robustness of the new tools. Due to a reasonably large database of faults, we can calculate frequencies of false and correct fault signals, and recommend implementations that optimize these empirical success rates. Next, we extend our proposed method for fault localization in a regional grid for scenarios where partial observability limits the available data. While classification methods have been proposed for fault localization, their effectiveness depends on the availability of labeled data, which is often impractical in real-life situations. Our approach bridges the gap between partial and full observability of the power grid. We develop efficient fault localization methods that can operate effectively even when only a subset of power grid bus data is available. This work contributes to the research area of fault diagnosis in scenarios where the number of available phasor measurement unit devices is smaller than the number of buses in the grid. We propose using Graph Neural Networks in combination with statistical fault localization methods to localize faults in a regional power grid with minimal available data. Our contribution to the field of fault localization aims to enable the adoption of effective fault localization methods for future power grids.
Embargo
Data-driven strategies for organic structure-property and structure-reactivity relationships
(Colorado State University. Libraries, 2024) Santhanalakkshmi Vejaykummar, Shree Sowndarya, author; Paton, Robert, advisor; Prasad, Ashok, committee member; Kim, Seonah, committee member; Nielsen, Aaron, committee member
The prediction of molecular properties plays a pivotal role in various domains, from drug discovery to materials science. With the advent of machine learning (ML) techniques, particularly in the field of cheminformatics, the prediction of properties for small organic molecules has witnessed significant advancements. This document delves into the diverse machine-learning strategies employed for the accurate prediction of properties crucial for understanding molecular behavior. In Chapter 1, I offer insights into the evolution of data-driven modeling through Quantitative Structure-Property Relationships (QSPR), highlighting promising advancements in utilizing chemical features to construct predictive models for molecular properties. In Chapter 2, I delve into the primary stage of modeling, focusing on data collection for predictive tasks. I illustrate how the integration of automation and computational tools' advancement can construct modular workflows for FAIR (Findable, Accessible, Interoperable, and Reusable) chemistry. This approach aims to enhance the usability and reproducibility of scientific data. In Chapter 3, I emphasize leveraging computational tools to access high-level data for small organic molecules. I showcase the creation of a novel metric for assessing organic radical stability, utilizing a comprehensive chemical database of radicals. This involves employing straightforward physical organic descriptors, namely fractional spin, and buried volume, computed through systematic computational workflows. In Chapter 4, I explore the progression of graph-based models designed to forecast molecular properties, specifically Bond Dissociation Energy. Additionally, I conduct a thorough examination of two particular applications pertinent to pharmaceutical and atmospheric chemistry. I demonstrate that utilizing a minimal number of molecules from the relevant chemical space can notably enhance large-scale machine-learning models. Finally, in Chapter 5, I combine the developed tools from Chapters 3 and 4, to perform goal-directed molecular optimization in identifying novel radicals for aqueous redox flow batteries using graph neural networks (radical stability, redox potentials, and bond dissociation energy) and reinforcement learning. This de novo molecular optimization strategy has successfully identified 32 new radical candidates. By amalgamating insights from diverse studies, this dissertation endeavors to offer a comprehensive grasp of how machine-learning strategies are transforming the terrain of molecular property prediction.
Open Access
Imprimitively generated designs
(Colorado State University. Libraries, 2022) Lear, Aaron, author; Betten, Anton, advisor; Adams, Henry, committee member; Nielsen, Aaron, committee member
Designs are a type of combinatorial object which uniformly cover all pairs in a base set V with subsets of V known as blocks. One important class of designs are those generated by a permutation group G acting on V and single initial block b subset of V. The most atomic examples of these designs would be generated by a primitive G. This thesis focuses on the less atomic case where G is imprimitive. Imprimitive permutation groups can be rearranged into a subset of easily understood groups which are derived from G and generate very symmetrical designs. This creates combinatorial restrictions on which group and block combinations can generate a design, turning a question about the existence of combinatorial objects into one more directly involving group theory. Specifically, the existence of imprimitively generated designs turns into a question about the existence of pair orbits of an appropriate size, for smaller permutation groups. This thesis introduces two restrictions on combinations of G and b which can generate designs, and discusses how they could be used to more efficiently enumerate imprimitively generated designs.