{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/49535"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/49535","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Intelligent distribution fault location using voltage magnitude measurements","abstract":"In an effort to increase situational awareness in the electric power grid, distributed monitoring devices are being implemented. These sensors allow the capture of fast-sampled voltage data such as frequency, voltage magnitude, and voltage angle measurements in a low-cost, easily deployable manner. Traditional fault location methods use both current and voltage data. Voltage monitoring devices only collect voltage data, but these sensors can collect this data anywhere along the distribution lines. An intelligent, iterative simulation-based fault location method using only voltage measurements, from anywhere on the line, is proposed. This technique involves comparing a measured voltage profile of a specific fault type and phase with a calculated voltage profile obtained through simulation of such a fault occurring at various locations with a range of probable fault impedances in the system. The simulated locations are intelligently selected using the Golden section search technique. The best match is determined using the L1-norm. The algorithm is successfully demonstrated using a PowerWorld case study.","abstract_html":"In an effort to increase situational awareness in the electric power grid, distributed monitoring devices are being implemented. These sensors allow the capture of fast-sampled voltage data such as frequency, voltage magnitude, and voltage angle measurements in a low-cost, easily deployable manner. Traditional fault location methods use both current and voltage data. Voltage monitoring devices only collect voltage data, but these sensors can collect this data anywhere along the distribution lines. An intelligent, iterative simulation-based fault location method using only voltage measurements, from anywhere on the line, is proposed. This technique involves comparing a measured voltage profile of a specific fault type and phase with a calculated voltage profile obtained through simulation of such a fault occurring at various locations with a range of probable fault impedances in the system. The simulated locations are intelligently selected using the Golden section search technique. The best match is determined using the L1-norm. The algorithm is successfully demonstrated using a PowerWorld case study.","abstract_has_math":false,"creators":["Hossain, Shamina"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Overbye, Thomas J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-05-30T16:48:48Z","date_published":"2014-05-30T16:48:48Z","updated_at":"2026-07-22T22:25:38Z","subjects":["Fault location","Distribution systems","Distributed voltage monitoring devices","Voltage sags"],"languages":["en"],"rights":["Copyright 2014 Shamina Shahrin Hossain"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/49535","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Overbye, Thomas J."]},{"key":"dc:creator","label":"Author","values":["Hossain, Shamina"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-05-30T16:48:48Z","2014-05"]},{"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":["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":["Fault location","Distribution systems","Distributed voltage monitoring devices","Voltage sags"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2014 Shamina Shahrin Hossain"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/49535"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In an effort to increase situational awareness in the electric power grid, distributed monitoring devices are being implemented. These sensors allow the capture of fast-sampled voltage data such as frequency, voltage magnitude, and voltage angle measurements in a low-cost, easily deployable manner. Traditional fault location methods use both current and voltage data. Voltage monitoring devices only collect voltage data, but these sensors can collect this data anywhere along the distribution lines. An intelligent, iterative simulation-based fault location method using only voltage measurements, from anywhere on the line, is proposed. This technique involves comparing a measured voltage profile of a specific fault type and phase with a calculated voltage profile obtained through simulation of such a fault occurring at various locations with a range of probable fault impedances in the system. The simulated locations are intelligently selected using the Golden section search technique. The best match is determined using the L1-norm. The algorithm is successfully demonstrated using a PowerWorld case study.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2014-04-17T15:33:00Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Hossain_Shamina.pdf: 1814714 bytes, checksum: a1a16e5fed3b235db52b7ad638c54308 (MD5)","Made available in DSpace on 2014-05-30T16:48:48Z (GMT). No. of bitstreams: 2 Shamina_Hossain.pdf: 1814714 bytes, checksum: a1a16e5fed3b235db52b7ad638c54308 (MD5) license.txt: 4065 bytes, checksum: 150e1a8420bc796939fe58f419bac427 (MD5)"]},{"key":"dc:title","label":"Title","values":["Intelligent distribution fault location using voltage magnitude measurements"]}]}],"canonical_facts":{"dc:contributor":["Overbye, Thomas J."],"dc:creator":["Hossain, Shamina"],"dc:date":["2014-05-30T16:48:48Z","2014-05"],"dc:description":["In an effort to increase situational awareness in the electric power grid, distributed monitoring devices are being implemented. These sensors allow the capture of fast-sampled voltage data such as frequency, voltage magnitude, and voltage angle measurements in a low-cost, easily deployable manner. Traditional fault location methods use both current and voltage data. Voltage monitoring devices only collect voltage data, but these sensors can collect this data anywhere along the distribution lines. An intelligent, iterative simulation-based fault location method using only voltage measurements, from anywhere on the line, is proposed. This technique involves comparing a measured voltage profile of a specific fault type and phase with a calculated voltage profile obtained through simulation of such a fault occurring at various locations with a range of probable fault impedances in the system. The simulated locations are intelligently selected using the Golden section search technique. The best match is determined using the L1-norm. The algorithm is successfully demonstrated using a PowerWorld case study.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2014-04-17T15:33:00Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Hossain_Shamina.pdf: 1814714 bytes, checksum: a1a16e5fed3b235db52b7ad638c54308 (MD5)","Made available in DSpace on 2014-05-30T16:48:48Z (GMT). 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