{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1971"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1971","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Automated detection and prediction of electrical disturbances in a power transmission system","abstract":"As power quality becomes a higher priority in the electric utility industry, utilities simply do not have the required personnel to analyze the ever-growing amount of data by hand. This thesis presents an automated approach for the analysis of power quality phenomena within a power transmission system by leveraging rule-based analytics as well as machine learning to analyze the characteristics of the recorded data. Waveform signatures analyzed within this thesis include: various faults, motor starting, and incipient instrument transformer failure. The developed analytics were tested on 160 waveform files and yielded an average accuracy of 99%. Machine learning techniques are also used to predict voltage unbalance on the transmission system above a certain threshold, which yielded an accuracy of over 91%. This work will result in time savings for engineers as well as increased reliability of the transmission system by providing near real-time detection, identification, and prevention of disturbances.","abstract_html":"As power quality becomes a higher priority in the electric utility industry, utilities simply do not have the required personnel to analyze the ever-growing amount of data by hand. This thesis presents an automated approach for the analysis of power quality phenomena within a power transmission system by leveraging rule-based analytics as well as machine learning to analyze the characteristics of the recorded data. Waveform signatures analyzed within this thesis include: various faults, motor starting, and incipient instrument transformer failure. The developed analytics were tested on 160 waveform files and yielded an average accuracy of 99%. Machine learning techniques are also used to predict voltage unbalance on the transmission system above a certain threshold, which yielded an accuracy of over 91%. This work will result in time savings for engineers as well as increased reliability of the transmission system by providing near real-time detection, identification, and prevention of disturbances.","abstract_has_math":false,"creators":["Boyd, Jonathan"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Reising, Donald R.","Disfani, Vahid R.; Karrar, Abdelrahman","College of Engineering and Computer Science"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05-01T07:00:00Z","date_published":"2024-05-01T07:00:00Z","updated_at":"2026-07-24T05:47:06Z","subjects":["Electric power system stability","Power transmission","Machine learning"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/792","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Reising, Donald R.","Disfani, Vahid R.; Karrar, Abdelrahman","College of Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Boyd, Jonathan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05-01T07:00:00Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-05-01T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electric power system stability","Power transmission","Machine learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/792"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Electrical Engineering","M. 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