{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/140556"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/140556","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"High-Resolution Time-Synchronized Monitoring and Anomaly Detection in Modern Distribution System","abstract":"The increasing penetration of renewable generation introduces new challenges to distribution system monitoring, including faster system dynamics and increased harmonic distortion. This dissertation aims to enhance traditional monitoring frameworks by developing high-resolution, cross-synchronized fundamental and harmonic measurement techniques that assist system operators in more effectively extracting additional dynamic information in modern power distribution systems. The first half of the dissertation focuses on developing advanced time-synchronized measurement techniques for identifying system-wide disturbances and dynamic behaviors. In Chapter 2, a cross-synchronized, frequency-adaptive synchrophasor estimation algorithm is developed to generate time-synchronized fundamental and harmonic phasors with high resolution, enabling real-time spectral analysis. The algorithm improves upon the traditional Discrete Fourier Transform (DFT) by employing adaptive windowing to suppress spectral leakage. In addition, its harmonic estimation accuracy is enhanced through the implementation of M-point average filters that mitigate the leakage from the fundamental component. In Chapter 3, an adaptive linear state estimator for unbalanced distribution systems is developed. A novel optimal PMU placement (OPP) scheme is proposed to guarantee complete observability of the system. The linear state estimator further improves its robustness under contingencies by adaptively reformulating its model to account for topology changes. The second half of the dissertation explores applications of the techniques developed in the first half. In Chapter 4, an SVM-based detector for transformer saturation caused by geomagnetically induced currents (GICs) is developed using synchronized harmonic real power derived from the harmonic synchrophasors estimated by the algorithm in Chapter 2. This approach offers a more cost-efficient alternative to direct GIC measurements. Finally, in Chapter 5, a detection and localization scheme for incipient faults is developed based on singular value decomposition (SVD). Faults are detected by tracking abrupt changes in singular values, and their locations are determined by analyzing correlations among the participations of each monitored bus in fault-related singular value variations.","abstract_html":"The increasing penetration of renewable generation introduces new challenges to distribution system monitoring, including faster system dynamics and increased harmonic distortion. This dissertation aims to enhance traditional monitoring frameworks by developing high-resolution, cross-synchronized fundamental and harmonic measurement techniques that assist system operators in more effectively extracting additional dynamic information in modern power distribution systems. The first half of the dissertation focuses on developing advanced time-synchronized measurement techniques for identifying system-wide disturbances and dynamic behaviors. In Chapter 2, a cross-synchronized, frequency-adaptive synchrophasor estimation algorithm is developed to generate time-synchronized fundamental and harmonic phasors with high resolution, enabling real-time spectral analysis. The algorithm improves upon the traditional Discrete Fourier Transform (DFT) by employing adaptive windowing to suppress spectral leakage. In addition, its harmonic estimation accuracy is enhanced through the implementation of M-point average filters that mitigate the leakage from the fundamental component. In Chapter 3, an adaptive linear state estimator for unbalanced distribution systems is developed. A novel optimal PMU placement (OPP) scheme is proposed to guarantee complete observability of the system. The linear state estimator further improves its robustness under contingencies by adaptively reformulating its model to account for topology changes. The second half of the dissertation explores applications of the techniques developed in the first half. In Chapter 4, an SVM-based detector for transformer saturation caused by geomagnetically induced currents (GICs) is developed using synchronized harmonic real power derived from the harmonic synchrophasors estimated by the algorithm in Chapter 2. This approach offers a more cost-efficient alternative to direct GIC measurements. Finally, in Chapter 5, a detection and localization scheme for incipient faults is developed based on singular value decomposition (SVD). Faults are detected by tracking abrupt changes in singular values, and their locations are determined by analyzing correlations among the participations of each monitored bus in fault-related singular value variations.","abstract_has_math":false,"creators":["Zhou, Yijie"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Electrical Engineering","degree_department":"Electrical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Centeno, Virgilio A."],"committee_members":["Kekatos, Vasileios","De La Reelopez, Jaime","Wicks, Alfred L.","Abbott, Amos L."],"year":2025,"date_issued":"2025-12-23","date_published":"2025-12-23","updated_at":"2026-07-22T22:20:26Z","subjects":["Synchrophasor","State Estimation","Support Vector Machine","Singular Value Decomposition"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45297"],"render_values":[{"text":"vt_gsexam:45297","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/140556","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Centeno, Virgilio A."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Kekatos, Vasileios","De La Reelopez, Jaime","Wicks, Alfred L.","Abbott, Amos L."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Zhou, Yijie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-12-24T09:00:26Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-12-24T09:00:26Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-23"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Synchrophasor","State Estimation","Support Vector Machine","Singular Value Decomposition"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45297"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/140556"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The increasing penetration of renewable generation introduces new challenges to distribution system monitoring, including faster system dynamics and increased harmonic distortion. This dissertation aims to enhance traditional monitoring frameworks by developing high-resolution, cross-synchronized fundamental and harmonic measurement techniques that assist system operators in more effectively extracting additional dynamic information in modern power distribution systems. The first half of the dissertation focuses on developing advanced time-synchronized measurement techniques for identifying system-wide disturbances and dynamic behaviors. In Chapter 2, a cross-synchronized, frequency-adaptive synchrophasor estimation algorithm is developed to generate time-synchronized fundamental and harmonic phasors with high resolution, enabling real-time spectral analysis. The algorithm improves upon the traditional Discrete Fourier Transform (DFT) by employing adaptive windowing to suppress spectral leakage. In addition, its harmonic estimation accuracy is enhanced through the implementation of M-point average filters that mitigate the leakage from the fundamental component. In Chapter 3, an adaptive linear state estimator for unbalanced distribution systems is developed. A novel optimal PMU placement (OPP) scheme is proposed to guarantee complete observability of the system. The linear state estimator further improves its robustness under contingencies by adaptively reformulating its model to account for topology changes. The second half of the dissertation explores applications of the techniques developed in the first half. In Chapter 4, an SVM-based detector for transformer saturation caused by geomagnetically induced currents (GICs) is developed using synchronized harmonic real power derived from the harmonic synchrophasors estimated by the algorithm in Chapter 2. This approach offers a more cost-efficient alternative to direct GIC measurements. Finally, in Chapter 5, a detection and localization scheme for incipient faults is developed based on singular value decomposition (SVD). Faults are detected by tracking abrupt changes in singular values, and their locations are determined by analyzing correlations among the participations of each monitored bus in fault-related singular value variations."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["The growing use of renewable energy and distributed generation is making the operation of modern power distribution systems increasingly complex. To help system operators better monitor system conditions and make informed decisions, this dissertation develops new techniques for grid monitoring and analysis. The first half of the dissertation focuses on high-speed, time-synchronized estimation of system states. In Chapter 2, an advanced algorithm is developed for phasor measurement units (PMUs)—a type of smart metering device—to provide accurate local measurements even under the challenging and rapidly changing conditions often seen in distribution systems. In Chapter 3, a state estimation algorithm is introduced to infer electrical quantities at locations that are not directly measured, providing system-wide situational awareness and improving visibility across the network. The second half of the dissertation explores practical applications of these techniques in detecting and analyzing grid anomalies. In Chapter 4, a machine learning–based detector is developed to monitor the impact of geomagnetic disturbances on power systems by identifying transformer saturations. In Chapter 5, a detection and localization scheme is proposed for incipient faults—subtle and developing faults that traditional monitoring systems often fail to capture. These applications demonstrate the effectiveness of the proposed monitoring framework in identifying and characterizing system anomalies, ultimately contributing to a smarter and more resilient electric grid."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["High-Resolution Time-Synchronized Monitoring and Anomaly Detection in Modern Distribution System"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Centeno, Virgilio A."],"dc:contributor.committeemember":["Kekatos, Vasileios","De La Reelopez, Jaime","Wicks, Alfred L.","Abbott, Amos L."],"dc:contributor.department":["Electrical Engineering"],"dc:creator":["Zhou, Yijie"],"dc:date.accessioned":["2025-12-24T09:00:26Z"],"dc:date.available":["2025-12-24T09:00:26Z"],"dc:date.issued":["2025-12-23"],"dc:description.abstract":["The increasing penetration of renewable generation introduces new challenges to distribution system monitoring, including faster system dynamics and increased harmonic distortion. This dissertation aims to enhance traditional monitoring frameworks by developing high-resolution, cross-synchronized fundamental and harmonic measurement techniques that assist system operators in more effectively extracting additional dynamic information in modern power distribution systems. The first half of the dissertation focuses on developing advanced time-synchronized measurement techniques for identifying system-wide disturbances and dynamic behaviors. In Chapter 2, a cross-synchronized, frequency-adaptive synchrophasor estimation algorithm is developed to generate time-synchronized fundamental and harmonic phasors with high resolution, enabling real-time spectral analysis. The algorithm improves upon the traditional Discrete Fourier Transform (DFT) by employing adaptive windowing to suppress spectral leakage. In addition, its harmonic estimation accuracy is enhanced through the implementation of M-point average filters that mitigate the leakage from the fundamental component. In Chapter 3, an adaptive linear state estimator for unbalanced distribution systems is developed. A novel optimal PMU placement (OPP) scheme is proposed to guarantee complete observability of the system. The linear state estimator further improves its robustness under contingencies by adaptively reformulating its model to account for topology changes. The second half of the dissertation explores applications of the techniques developed in the first half. In Chapter 4, an SVM-based detector for transformer saturation caused by geomagnetically induced currents (GICs) is developed using synchronized harmonic real power derived from the harmonic synchrophasors estimated by the algorithm in Chapter 2. This approach offers a more cost-efficient alternative to direct GIC measurements. Finally, in Chapter 5, a detection and localization scheme for incipient faults is developed based on singular value decomposition (SVD). Faults are detected by tracking abrupt changes in singular values, and their locations are determined by analyzing correlations among the participations of each monitored bus in fault-related singular value variations."],"dc:description.abstractgeneral":["The growing use of renewable energy and distributed generation is making the operation of modern power distribution systems increasingly complex. To help system operators better monitor system conditions and make informed decisions, this dissertation develops new techniques for grid monitoring and analysis. The first half of the dissertation focuses on high-speed, time-synchronized estimation of system states. In Chapter 2, an advanced algorithm is developed for phasor measurement units (PMUs)—a type of smart metering device—to provide accurate local measurements even under the challenging and rapidly changing conditions often seen in distribution systems. In Chapter 3, a state estimation algorithm is introduced to infer electrical quantities at locations that are not directly measured, providing system-wide situational awareness and improving visibility across the network. The second half of the dissertation explores practical applications of these techniques in detecting and analyzing grid anomalies. In Chapter 4, a machine learning–based detector is developed to monitor the impact of geomagnetic disturbances on power systems by identifying transformer saturations. In Chapter 5, a detection and localization scheme is proposed for incipient faults—subtle and developing faults that traditional monitoring systems often fail to capture. These applications demonstrate the effectiveness of the proposed monitoring framework in identifying and characterizing system anomalies, ultimately contributing to a smarter and more resilient electric grid."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45297"],"dc:identifier.uri":["https://hdl.handle.net/10919/140556"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Synchrophasor","State Estimation","Support Vector Machine","Singular Value Decomposition"],"dc:title":["High-Resolution Time-Synchronized Monitoring and Anomaly Detection in Modern Distribution System"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:26Z"}