Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Data-driven approaches for distribution grid modernization: exploring state estimaion, pseudo-measurement generation and false data detection(Montana State University - Bozeman, College of Engineering, 2023) Radhoush, Sepideh; Chairperson, Graduate Committee: Brad WhitakerDistribution networks must be regularly updated to enhance their performance and meet customer electricity requirements. Advanced technologies and infrastructure--including two- way communication, smart measuring devices, distributed generations in various forms, electric vehicles, variable loads, etc.--have been added to improve the overall efficiency of distribution networks. Corresponding to these new features and structures, the continuous control and monitoring of distribution networks should be intensified to keep track of any modifications to the distribution network performance. Distribution system state estimation has been introduced for real-time monitoring of distribution networks. State estimation calculations are highly dependent on measurement data which are collected from measurement devices in distribution networks. However, the installation of measurement devices is not possible at all buses to ensure the distribution network is fully observable. To address the lack of real measurements, pseudo- measurements are produced from historical load and generation data. Available measurements, along with physical distribution network topology, are fed into a state estimation algorithm to determine system state variables. Then, state estimation results are sent to a control center for further processing to enhance distribution network operation. However, the accuracy of state estimation results could be degraded by false data injection attacks on measurement data. If these attacks are not detected, distribution network operation could be significantly influenced. Different methods have been developed to enhance a distribution network operation and management. Machine learning approaches have also been identified to be beneficial in solving different types of problems in a power grid. In this dissertation, machine learning is applied to three areas of distribution systems: generating pseudo-measurements, performing distribution system state estimation calculations, and detecting false data injection attacks on measurement data. In addition to addressing these areas individually, machine learning is used to simultaneously perform distribution system state estimation calculation and false data injection attack detection. This is done by taking advantage of conventional and smart measurement data at different time scales. The results reveal that the operation and performance of a distribution network are improved using machine learning algorithms, leading to more effective power grid modernization.Item Automatic 2D material detection and quantum emission prediction using deep learning-based models(Montana State University - Bozeman, College of Engineering, 2023) Ramezani, Fereshteh; Chairperson, Graduate Committee: Brad WhitakerThe realm of quantum engineering holds immense promise for revolutionizing technological landscapes, particularly with the advent of 2D materials in quantum device applications. The fundamental properties of these materials make them pivotal in various quantum applications. However, the progress in quantum engineering faces significant roadblocks, primarily centered around two challenges: accurate 2D material detection and understanding the random nature of quantum fluctuations. In response to the first challenge, I have successfully implemented a new deep learning pipeline to identify 2D materials in microscopic images. I have used a state-of-the-art two-stage object detector and trained it on images containing flakes of varying thickness of hexagonal boron nitride (hBN, a 2D material). The trained model achieved a high detection accuracy for the rare category of thin flakes (< or = 50 atomic layers thick). My further analysis shows that this proposed pipeline is robust against changes in color or substrate background, and could be generalized to various microscope settings. As an achievement, I have integrated my proposed method to the 2D quantum material pipeline (2D-QMaP), that has been under development by the MonArk Quantum Foundry, to provide automated capabilities that unite and accelerate the primary stages of sample preparation and device fabrication for 2D quantum materials research. My proposed algorithm has given the 2D-QMaP fully automatic real-time 2D flake detection capabilities, which has never been done effectively before. To address the second challenge, I assessed the random nature of quantum fluctuations, and I developed time series forecasting deep learning models to analyze and predict quantum emission fluctuations for the first time. My trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies. The automated 2D material identification, addressing the laborious process of flake detection, and the introduction of innovative quantum fluctuations analysis with predictive capabilities not only streamline research processes but also hold the promise of creating more stable and dependable quantum emission devices, thus significantly advancing the broader field of quantum engineering.Item Machine learning pipeline for rare-event detection in synthetic-aperture radar and LIDAR data(Montana State University - Bozeman, College of Engineering, 2021) Scofield, Trey Palmer; Chairperson, Graduate Committee: Brad WhitakerIn this work, we develop a machine learning pipeline to autonomously classify synthetic aperture radar (SAR) and lidar data in rare-event, remote sensing applications. Here, we are predicting the presence of volcanoes on the surface of Venus, fish in Yellowstone Lake, and select marine-life in the Gulf of Mexico. Given the efficiency of collecting SAR images in space and airborne lidar geographical surveys, the size of the datasets are immense. Immense training data is desirable for machine learning models; however, a large majority of the data we are using do not contain volcanoes or fish, respectively. Thus, the machine learning models must be formulated in such a way to place a high emphasis on the minority, target classes. The developed pipeline includes data preprocessing, unsupervised clustering, feature extraction, and classification. For each collection of data, sub-images are initially fed through the pipeline to capture fine detail characteristics until they are mapped back to their original image to identify overall region behavior and the location of the target class(es). For both sub-images and original images, results were quantified and the most effective algorithm combinations and parameters were assigned. In this analysis, we determined the classification results are not sufficient enough to propel a completely autonomous system, rather, some manual observing of the data will need to be performed. Nonetheless, the pipeline serves as an effective tool to reduce costs associated with electronic storage and transmission of the data, as well as human labor in manually inspecting the data. It does this by removing a majority of the unimportant, non-target data in some cases while successfully retaining a high percentage of the important images.Item Weed and crop discrimination with hyperspectral imaging and machine learning(Montana State University - Bozeman, College of Engineering, 2019) Scherrer, Bryan Joseph; Chairperson, Graduate Committee: Joseph A. ShawHerbicide-resistant weed biotypes are spreading across crop fields nationally and internationally and mapping them with traditional crop science methods - cloning plants and testing their resistance levels in a lab - are costly and time consuming. A segment of the field of precision agriculture is being developed to accurately and quickly map the location of herbicide-resistant and herbicide-susceptible weeds using advanced optics and computer algorithms. In our study, we collected hundreds of thousands of spectra of herbicide-resistant and herbicide-susceptible biotypes of the weeds kochia, mare's tail and lamb's quarter and of crops including barley, corn, dry pea, garbanzo, lentils, pinto bean, safflower, sugar beet at the Southern Agricultural Research Center in Huntley, Montana using a hyperspectral imager. Plants were imaged in a controlled greenhouse setting as well as in crop fields using ground-based and drone-based imaging platforms. The spectra were differentiated from one another using a feedforward neural network machine learning algorithm. Classification accuracies depended on what plants were imaged, the age of the plants and lighting conditions of the experiment. They ranged from 77% to 99% for spectra acquired on our ground-based imaging platform and from 25% to 79% on our drone- based platform.Item A customizable artificial auditory fovea(Montana State University - Bozeman, College of Engineering, 2018) Casebeer, Christopher Ness; Chairperson, Graduate Committee: Ross K. SniderWe, as humans, can separate and attend to audio sources in mixtures of sounds and noise. We can listen distinctly to a friend at a party in a sea of background noise and conversations. Human auditory neurology exceeds even state-of-the-art audio algorithms. How are we able to do this? This dissertation takes inspiration from biology to frame a novel audio processing front-end. Neurobiology shows that auditory neurons isolate signal onsets, timing, frequency, amplitude and modulation characteristics. Why is it then that many standard processing methods choose to ignore this information or make the assumption that machine learning will extract it regardless of input processing? This dissertation uses time-frequency analysis principles towards building a new front-end aimed at preserving these fine temporal and spectral details of the original signal to improve audio system detection and recognition. The system allows keeping the fine frequency and time characteristics of a signal during analysis, while allowing customization of how much and where this resolution is kept. Like biology, this front-end can dedicate resources to detecting important signal events. It can over represent or foveate regions of the time-frequency plane that are important to the signal processing task at hand. These fine details are hypothesized to help enable audio learning algorithms to detect the fine nuances that distinguish musical instruments, determine the characteristics of a specific persons voice, or even detect the emotional state of a person. This customizable auditory fovea aims to mimic the powerful detection capability found in biology which is in contrast to standard methods in audio signal processing.Item Market-based power management and control of resilient smart grids and microgrids using a game theoretic multi-agent system approach(Montana State University - Bozeman, College of Engineering, 2017) Dehghanpour, Kaveh; Chairperson, Graduate Committee: M. Hashem Nehrir; Hashem Nehrir, John Sheppard and Nathan Kelly were co-authors of the article, 'Agent-based modeling in electrical energy markets using dynamic bayesian networks' in the journal 'IEEE transaction on power systems' which is contained within this thesis.; Hashem Nehrir, John Sheppard and Nathan Kelly were co-authors of the article, 'Agent-based modeling of retail electrical energy markets with demand response' in the journal 'IEEE transaction on smart grid' which is contained within this thesis.; Hashem Nehrir were co-authors of the article, 'Intelligent microgrid power management using the concept of nash bargaining solution' in 'Intelligent System Applications to Power Systems (ISAP) Conference, 2017' which is contained within this thesis.; Hashem Nehrir was a co-author of the article, 'Real-time multiobjective microgrid power management using distributed optimization in an agent-based bargaining framework' in the journal 'IEEE transaction on smart grid' which is contained within this thesis.; Hashem Nehrir was a co-author of the article, 'An agent-based hierarchical bargaining framework for power management of multiple cooperative microgrids' in the journal 'IEEE transaction on smart grid' which is contained within this thesis.; Hashem Nehrir was a co-author of the article, 'A market-based resilient power management technique for distribution systems with multiple microgrids using a multi-agent system approach' submitted to the journal 'Electric power components and systems' which is contained within this thesis.In this dissertation, we address the problem of optimal resource allocation through distributed market-based techniques in future power systems. Several connected problems are considered in different stages of this research project: 1) optimal decision making of generation companies in wholesale markets, 2) demand response and energy pricing in retail markets, 3) single and multiple microgrid power management within the retail sector, and 4) introducing a resiliency-aware power management for microgrid-based distribution systems. Hence, this work addresses the challenges that are connected to the economics of energy. All of these problems are concerned with optimizing the behavior of the participants in electricity markets (in wholesale and retail sectors) to achieve certain objectives in power system operation (e.g., profit maximization, peak shaving, resiliency improvement, extreme event awareness and preparedness, etc.). Solution concepts from the fields of machine learning and game theory have been employed to address these problems. Moreover, distributed agent-based frameworks are designed for modeling the interactive decision making processes and implementing control laws in electricity markets. The goal is to introduce automated 'intelligent' decision making capabilities into power systems to improve the efficiency of grid operation. This is closely related to the concept of 'smart grids'. The proposed solution strategies are verified through numerical simulations in MATLAB environment. The results demonstrate that using the proposed distributed decision tools and machine-learning-based techniques we can improve the performance of power systems to achieve higher levels of controllability and efficiency, while enhancing system resiliency.