{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/92738"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/92738","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Real-time power system topology change detection and identification","abstract":"This thesis proposes a framework for detection and identification of system topological changes in near real-time that utilizes the statistical properties of electricity generation and demand, which are assumed to be known. Instead of relying on offline models as with traditional methods, the proposed method is model-free, and exploits the high-speed synchronized measurements provided by phasor measurement units (PMUs). In this framework, a statistical quickest change algorithm is applied to the voltage phase angle measurements collected from PMUs to detect the change-point that corresponds to the system topology change instant. An advantage of this algorithm is that the operator also has full control over the tradeoff between detection delay and false alarm rate. Additionally, a full measurement set is not necessary for its implementation and good results can be achieved even for a few PMU measurements. A scheme for systematic PMU bus selection is presented along with a method to partition the power system such that the aforementioned algorithm for line outage detection can be applied in parallel to each area, allowing for even faster detection. The optimal partitioning scheme is formulated as an integer program and solved using a greedy algorithm. In the second half of the thesis, an adaptive line outage detection algorithm that accounts for the transient dynamics following a line outage is proposed. A more accurate governor power flow model of the power system is used. This new algorithm is shown to have better performance compared to existing algorithms for line outage detection. In order to lend support for the work done in this thesis, case studies are done through simulations on standard IEEE test systems.","abstract_html":"This thesis proposes a framework for detection and identification of system topological changes in near real-time that utilizes the statistical properties of electricity generation and demand, which are assumed to be known. Instead of relying on offline models as with traditional methods, the proposed method is model-free, and exploits the high-speed synchronized measurements provided by phasor measurement units (PMUs). In this framework, a statistical quickest change algorithm is applied to the voltage phase angle measurements collected from PMUs to detect the change-point that corresponds to the system topology change instant. An advantage of this algorithm is that the operator also has full control over the tradeoff between detection delay and false alarm rate. Additionally, a full measurement set is not necessary for its implementation and good results can be achieved even for a few PMU measurements. A scheme for systematic PMU bus selection is presented along with a method to partition the power system such that the aforementioned algorithm for line outage detection can be applied in parallel to each area, allowing for even faster detection. The optimal partitioning scheme is formulated as an integer program and solved using a greedy algorithm. In the second half of the thesis, an adaptive line outage detection algorithm that accounts for the transient dynamics following a line outage is proposed. A more accurate governor power flow model of the power system is used. This new algorithm is shown to have better performance compared to existing algorithms for line outage detection. In order to lend support for the work done in this thesis, case studies are done through simulations on standard IEEE test systems.","abstract_has_math":false,"creators":["Jiang, Xichen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Domínguez-García, Alejandro","Zhu, Hao","Sauer, Peter","Veeravalli, Venugopal"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-11-10T17:49:59Z","date_published":"2016-11-10T17:49:59Z","updated_at":"2026-07-22T22:26:35Z","subjects":["Phasor measurement units (PMU)","Line Outage","Detection","Identification","Estimation","CuSum","Quickest Change","Topology Change"],"languages":["en"],"rights":["Copyright 2016 Xichen Jiang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/92738","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Domínguez-García, Alejandro","Zhu, Hao","Sauer, Peter","Veeravalli, Venugopal"]},{"key":"dc:creator","label":"Author","values":["Jiang, Xichen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-11-10T17:49:59Z","2016-07-07","2016-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Phasor measurement units (PMU)","Line Outage","Detection","Identification","Estimation","CuSum","Quickest Change","Topology Change"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Xichen Jiang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/92738"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis proposes a framework for detection and identification of system topological changes in near real-time that utilizes the statistical properties of electricity generation and demand, which are assumed to be known. Instead of relying on offline models as with traditional methods, the proposed method is model-free, and exploits the high-speed synchronized measurements provided by phasor measurement units (PMUs). In this framework, a statistical quickest change algorithm is applied to the voltage phase angle measurements collected from PMUs to detect the change-point that corresponds to the system topology change instant. An advantage of this algorithm is that the operator also has full control over the tradeoff between detection delay and false alarm rate. Additionally, a full measurement set is not necessary for its implementation and good results can be achieved even for a few PMU measurements. A scheme for systematic PMU bus selection is presented along with a method to partition the power system such that the aforementioned algorithm for line outage detection can be applied in parallel to each area, allowing for even faster detection. The optimal partitioning scheme is formulated as an integer program and solved using a greedy algorithm. In the second half of the thesis, an adaptive line outage detection algorithm that accounts for the transient dynamics following a line outage is proposed. A more accurate governor power flow model of the power system is used. This new algorithm is shown to have better performance compared to existing algorithms for line outage detection. In order to lend support for the work done in this thesis, case studies are done through simulations on standard IEEE test systems.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-11-09 without embargo terms","The student, Xichen Jiang, accepted the attached license on 2016-06-30 at 10:25.","The student, Xichen Jiang, submitted this Dissertation for approval on 2016-06-30 at 10:31.","This Dissertation was approved for publication on 2016-07-07 at 15:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9729 on 2016-11-09 at 10:22:06","Made available in DSpace on 2016-11-10T17:49:59Z (GMT). No. of bitstreams: 2 JIANG-DISSERTATION-2016.pdf: 1007918 bytes, checksum: 682bcdb179050a8ecf0696fc0dad0ba8 (MD5) LICENSE.txt: 4209 bytes, checksum: 7e7a631e40215c124f707742352174ae (MD5) Previous issue date: 2016-07-07"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Real-time power system topology change detection and identification"]}]}],"canonical_facts":{"dc:contributor":["Domínguez-García, Alejandro","Zhu, Hao","Sauer, Peter","Veeravalli, Venugopal"],"dc:creator":["Jiang, Xichen"],"dc:date":["2016-11-10T17:49:59Z","2016-07-07","2016-08"],"dc:description":["This thesis proposes a framework for detection and identification of system topological changes in near real-time that utilizes the statistical properties of electricity generation and demand, which are assumed to be known. Instead of relying on offline models as with traditional methods, the proposed method is model-free, and exploits the high-speed synchronized measurements provided by phasor measurement units (PMUs). In this framework, a statistical quickest change algorithm is applied to the voltage phase angle measurements collected from PMUs to detect the change-point that corresponds to the system topology change instant. An advantage of this algorithm is that the operator also has full control over the tradeoff between detection delay and false alarm rate. Additionally, a full measurement set is not necessary for its implementation and good results can be achieved even for a few PMU measurements. A scheme for systematic PMU bus selection is presented along with a method to partition the power system such that the aforementioned algorithm for line outage detection can be applied in parallel to each area, allowing for even faster detection. The optimal partitioning scheme is formulated as an integer program and solved using a greedy algorithm. In the second half of the thesis, an adaptive line outage detection algorithm that accounts for the transient dynamics following a line outage is proposed. A more accurate governor power flow model of the power system is used. This new algorithm is shown to have better performance compared to existing algorithms for line outage detection. In order to lend support for the work done in this thesis, case studies are done through simulations on standard IEEE test systems.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-11-09 without embargo terms","The student, Xichen Jiang, accepted the attached license on 2016-06-30 at 10:25.","The student, Xichen Jiang, submitted this Dissertation for approval on 2016-06-30 at 10:31.","This Dissertation was approved for publication on 2016-07-07 at 15:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9729 on 2016-11-09 at 10:22:06","Made available in DSpace on 2016-11-10T17:49:59Z (GMT). No. of bitstreams: 2 JIANG-DISSERTATION-2016.pdf: 1007918 bytes, checksum: 682bcdb179050a8ecf0696fc0dad0ba8 (MD5) LICENSE.txt: 4209 bytes, checksum: 7e7a631e40215c124f707742352174ae (MD5) Previous issue date: 2016-07-07"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/92738"],"dc:language":["en"],"dc:rights":["Copyright 2016 Xichen Jiang"],"dc:subject":["Phasor measurement units (PMU)","Line Outage","Detection","Identification","Estimation","CuSum","Quickest Change","Topology Change"],"dc:title":["Real-time power system topology change detection and identification"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:35Z"}