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Learning From Multi-Modal Spatiotemporal Data : = Machine Learning Approaches to Advance Resilience in Smart Grids.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Learning From Multi-Modal Spatiotemporal Data :/
Reminder of title:	Machine Learning Approaches to Advance Resilience in Smart Grids.
Author:	Alqudah, Mohammad Khaled.
Description:	1 online resource (137 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-07, Section: B.
Contained By:	Dissertations Abstracts International85-07B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798381381979

Learning From Multi-Modal Spatiotemporal Data : = Machine Learning Approaches to Advance Resilience in Smart Grids.
Alqudah, Mohammad Khaled.

Learning From Multi-Modal Spatiotemporal Data :Machine Learning Approaches to Advance Resilience in Smart Grids. - 1 online resource (137 pages)

Source: Dissertations Abstracts International, Volume: 85-07, Section: B.

Thesis (Ph.D.)--Temple University, 2023.

Includes bibliographical references

The 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 in (Alqudah et al., 2020). 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 in (Alqudah et al., 2022b) 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. In (Alqudah et al., 2022a), 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 in (Alqudah & Obradovic, 2023) 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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381381979Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Climate changeIndex Terms--Genre/Form:

554714
Electronic books.

Learning From Multi-Modal Spatiotemporal Data : = Machine Learning Approaches to Advance Resilience in Smart Grids.
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520 $a The 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 in (Alqudah et al., 2020). 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 in (Alqudah et al., 2022b) 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. In (Alqudah et al., 2022a), 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 in (Alqudah & Obradovic, 2023) 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.
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