{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/87999"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/87999","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Big data in power systems: a statistical approach on synchrophasor application","abstract":"Phasor Measurement Units (PMUs) are regarded as one of the most valuable device on the power system. PMUs, being devices that are found on buses of the power system, are capable of supplying both detailed and broad situational information. The data produced by PMUs, which are synchronized by GPS timing, are referred to as synchrophasor data, and have plethora of information about the power system that has traditionally not been observed. The general trend in the use of synchrophasor data is offline, such as model validation and post-event analysis. This thesis aims to provide a significantly different approach in the synchrophasor usage. Rather than considering synchrophasor data as extra sensor data that allows for enhancement of models, this thesis treats synchrophasor data as a type of big data, and utilizes statistical methods to find solutions and insight to synchrophasor data. The chapters of this thesis start with applying statistical methods to offline usage, and continue to consider statistical methods in real-time application.","abstract_html":"Phasor Measurement Units (PMUs) are regarded as one of the most valuable device on the power system. PMUs, being devices that are found on buses of the power system, are capable of supplying both detailed and broad situational information. The data produced by PMUs, which are synchronized by GPS timing, are referred to as synchrophasor data, and have plethora of information about the power system that has traditionally not been observed. The general trend in the use of synchrophasor data is offline, such as model validation and post-event analysis. This thesis aims to provide a significantly different approach in the synchrophasor usage. Rather than considering synchrophasor data as extra sensor data that allows for enhancement of models, this thesis treats synchrophasor data as a type of big data, and utilizes statistical methods to find solutions and insight to synchrophasor data. The chapters of this thesis start with applying statistical methods to offline usage, and continue to consider statistical methods in real-time application.","abstract_has_math":false,"creators":["Kirihara, Kenta"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engineering","degree_department":null,"school":null,"contributors":["Sauer, Peter W."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-29T20:38:04Z","date_published":"2015-09-29T20:38:04Z","updated_at":"2026-07-22T22:26:31Z","subjects":["Synchrophasor","Phasor Measurement Units (PMU)","big data","statistics"],"languages":["en"],"rights":["Copyright 2015 Kenta Kirihara"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/87999","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sauer, Peter W."]},{"key":"dc:creator","label":"Author","values":["Kirihara, Kenta"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-29T20:38:04Z","2015-08","2015-07-09","2015-8"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Synchrophasor","Phasor Measurement Units (PMU)","big data","statistics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Kenta Kirihara"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/87999"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Phasor Measurement Units (PMUs) are regarded as one of the most valuable device on the power system. PMUs, being devices that are found on buses of the power system, are capable of supplying both detailed and broad situational information. The data produced by PMUs, which are synchronized by GPS timing, are referred to as synchrophasor data, and have plethora of information about the power system that has traditionally not been observed. The general trend in the use of synchrophasor data is offline, such as model validation and post-event analysis. This thesis aims to provide a significantly different approach in the synchrophasor usage. Rather than considering synchrophasor data as extra sensor data that allows for enhancement of models, this thesis treats synchrophasor data as a type of big data, and utilizes statistical methods to find solutions and insight to synchrophasor data. 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The data produced by PMUs, which are synchronized by GPS timing, are referred to as synchrophasor data, and have plethora of information about the power system that has traditionally not been observed. The general trend in the use of synchrophasor data is offline, such as model validation and post-event analysis. This thesis aims to provide a significantly different approach in the synchrophasor usage. Rather than considering synchrophasor data as extra sensor data that allows for enhancement of models, this thesis treats synchrophasor data as a type of big data, and utilizes statistical methods to find solutions and insight to synchrophasor data. The chapters of this thesis start with applying statistical methods to offline usage, and continue to consider statistical methods in real-time application.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2015-09-29 without embargo terms","The student, Kenta Kirihara, accepted the attached license on 2015-07-06 at 09:12.","The student, Kenta Kirihara, submitted this Thesis for approval on 2015-07-06 at 09:28.","This Thesis was approved for publication on 2015-07-09 at 15:48.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8347 on 2015-09-29 at 13:22:07","Made available in DSpace on 2015-09-29T20:38:04Z (GMT). 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