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dc.contributor.advisorObradovic, Zoran
dc.creatorAlqudah, Mohammad
dc.date.accessioned2024-01-11T21:40:59Z
dc.date.available2024-01-11T21:40:59Z
dc.date.issued2023-12
dc.identifier.urihttp://hdl.handle.net/20.500.12613/9541
dc.description.abstractThe electric grid has been expanding both in size and the technologies used. As of the 2020s, the United States power grid consists of more than 9,200 electric generating units with more than 1 million megawatts of generating capacity connected to more than 300,000 miles of transmission lines. The United States electricity grid has rapidly expanded in recent decades, and the majority (over 70\%) of its infrastructure has exceeded 25 years of age. Due to its size and age, several challenges have emerged. Widespread power outages have been increasing across the United States. Between 2018 and 2020, more than 231,000 power outages occurred in the United States that lasted more than one hour, out of which 17,484 lasted at least eight hours. In the same period, the power outages resulted in an annual loss of 520 million customer hours across 2,447 U.S. counties. Moreover, and with the rapidly changing climate, between 2000 and 2021, approximately 83\% of significant power outages impacting a minimum of 50,000 customers in the United States were attributed to severe weather conditions. Lastly, the increasing use of renewables and other non-traditional generation methods forces the power system towards a more decentralized model, with many integrated systems constantly added to the grid. This decentralization adds additional burdens on controlling systems and grid operators. The rapid growth of technology and data storage allowed the deployment of sensing devices across the electric grid. Such technologies present a golden opportunity to tackle many of the electric grid's challenges. Despite that, such technologies presented many challenges simultaneously. With the large amounts of data, it became humanly impossible to comprehend, analyze, and use all collected data manually. While machine learning can be used to analyze smart grid data, this can be challenged by the nature of its data. Smart grid produces high-dimensional spatiotemporal data, and many applications require multi-modal data. Moreover, power systems' data quality challenges add complexities to model development. The data is noisy, contains missing segments, and usually has incomplete and inaccurate labels. In addition, interpreting machine learning models in the context of smart grids poses unique challenges. To address these challenges, different models for multiple smart-grid applications were introduced in this research, where each model focused on producing practical solutions for the challenges facing current-day smart grids. Using spatiotemporal data, a solar generation prediction model was proposed. The solution combined spatial and temporal data, then utilized machine learning embeddings to build datasets to train downstream models. This resulted in accurate prediction of solar generation across several settings. In addition to solar generation prediction, several models were introduced to detect, predict and explain power grid faults. A neural model is introduced to detect power faults from Phasor Measurement Unit (PMU) data. A novel method is introduced to preprocesses, de-noise, and combine high dimensional data, then this data is used to train novel neural methods that detect faults in multiple settings. This model addressed issues of high dimensionality and data quality. After that, several models studying power fault prediction and precursor discovery were introduced. A model that jointly predicts outages 6 hours ahead and produces explainable event precursors from multi-modal data is introduced. Where such precursors can assist power grid operators to take action to mitigate widespread power outages. Finally, a novel methodology is introduced that expands to previous work by predicting and extracting event precursors spatiotemporally 12 hours in advance. Where event precursors can be predicted on multiple spatial locations simultaneously, extracted spatiotemporal event precursors can help grid operators narrow down mitigation plans and help reduce the risk of widespread power outages.
dc.format.extent136 pages
dc.language.isoeng
dc.publisherTemple University. Libraries
dc.relation.ispartofTheses and Dissertations
dc.rightsIN COPYRIGHT- This Rights Statement can be used for an Item that is in copyright. Using this statement implies that the organization making this Item available has determined that the Item is in copyright and either is the rights-holder, has obtained permission from the rights-holder(s) to make their Work(s) available, or makes the Item available under an exception or limitation to copyright (including Fair Use) that entitles it to make the Item available.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectComputer science
dc.subjectArtificial intelligence
dc.subjectInformation science
dc.subjectClassification
dc.subjectClimate change
dc.subjectEvent precursors
dc.subjectEvent prediction
dc.subjectMachine learning
dc.subjectSpatiotemporal data
dc.titleLearning from multi-modal spatiotemporal data: machine learning approaches to advance resilience in smart grids
dc.typeText
dc.type.genreThesis/Dissertation
dc.contributor.committeememberVucetic, Slobodan
dc.contributor.committeememberGao, Hongchang
dc.contributor.committeememberDu, Liang
dc.description.departmentComputer and Information Science
dc.relation.doihttp://dx.doi.org/10.34944/dspace/9503
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.description.degreePh.D.
dc.identifier.proqst15487
dc.creator.orcid0000-0001-7011-3762
dc.date.updated2024-01-09T14:05:32Z
refterms.dateFOA2024-01-11T21:41:00Z
dc.identifier.filenameAlqudah_temple_0225E_15487.pdf


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