{"id":{"repo_id":"uno","oai_identifier":"oai:scholarworks.uno.edu:td-2262"},"canonical_url":"https://search.dev.ndltd.org/etd/uno/oai:scholarworks.uno.edu:td-2262","repository":{"repo_id":"uno","name":"University of New Orleans","base_url":"https://scholarworks.uno.edu/do/oai/"},"display":{"title":"Pattern Recognition of Power Systems Voltage Stability Using Real Time Simulations","abstract":"The basic idea deals with detecting the voltage collapse ahead of time to provide the operators a lead time for remedial actions and for possible prevention of blackouts. To detect cases of voltage collapse, we shall create methods using pattern recognition in conjunction with real time simulation of case studies and shall develop heuristic methods for separating voltage stable cases from voltage unstable cases that result in response to system contingencies and faults. Using Real Time Simulator in Entergy-UNO Power & Energy Research Laboratory, we shall simulate several contingencies on IEEE 39-Bus Test System and compile the results in two categories of stable and unstable voltage cases. The second stage of the proposed work mainly deals with the study of different patterns of voltage using artificial neural networks. The final stage deals with the training of the controllers in order to detect stability of power system in advance.","abstract_html":"The basic idea deals with detecting the voltage collapse ahead of time to provide the operators a lead time for remedial actions and for possible prevention of blackouts. To detect cases of voltage collapse, we shall create methods using pattern recognition in conjunction with real time simulation of case studies and shall develop heuristic methods for separating voltage stable cases from voltage unstable cases that result in response to system contingencies and faults. Using Real Time Simulator in Entergy-UNO Power & Energy Research Laboratory, we shall simulate several contingencies on IEEE 39-Bus Test System and compile the results in two categories of stable and unstable voltage cases. The second stage of the proposed work mainly deals with the study of different patterns of voltage using artificial neural networks. The final stage deals with the training of the controllers in order to detect stability of power system in advance.","abstract_has_math":false,"creators":["Beeravolu, Nagendrakumar"],"institution":null,"degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Rastgoufard, Parviz","Leevongwat, Ittiphong","Bourgeois, Edit J."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-12-17T08:00:00Z","date_published":"2010-12-17T08:00:00Z","updated_at":"2026-07-24T05:29:15Z","subjects":["Real time simulator","Voltage stability","Voltage collapse","Blackout","Pattern recognition","Hypersim","EMTP"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uno.edu/td/1279","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Rastgoufard, Parviz","Leevongwat, Ittiphong","Bourgeois, Edit J."]},{"key":"dc:creator","label":"Author","values":["Beeravolu, Nagendrakumar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Real time simulator","Voltage stability","Voltage collapse","Blackout","Pattern recognition","Hypersim","EMTP"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uno.edu/td/1279"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The basic idea deals with detecting the voltage collapse ahead of time to provide the operators a lead time for remedial actions and for possible prevention of blackouts. To detect cases of voltage collapse, we shall create methods using pattern recognition in conjunction with real time simulation of case studies and shall develop heuristic methods for separating voltage stable cases from voltage unstable cases that result in response to system contingencies and faults. Using Real Time Simulator in Entergy-UNO Power & Energy Research Laboratory, we shall simulate several contingencies on IEEE 39-Bus Test System and compile the results in two categories of stable and unstable voltage cases. The second stage of the proposed work mainly deals with the study of different patterns of voltage using artificial neural networks. The final stage deals with the training of the controllers in order to detect stability of power system in advance."]},{"key":"dc:title","label":"Title","values":["Pattern Recognition of Power Systems Voltage Stability Using Real Time Simulations"]}]}],"canonical_facts":{"dc:contributor":["Rastgoufard, Parviz","Leevongwat, Ittiphong","Bourgeois, Edit J."],"dc:creator":["Beeravolu, Nagendrakumar"],"dc:description.abstract":["The basic idea deals with detecting the voltage collapse ahead of time to provide the operators a lead time for remedial actions and for possible prevention of blackouts. To detect cases of voltage collapse, we shall create methods using pattern recognition in conjunction with real time simulation of case studies and shall develop heuristic methods for separating voltage stable cases from voltage unstable cases that result in response to system contingencies and faults. Using Real Time Simulator in Entergy-UNO Power & Energy Research Laboratory, we shall simulate several contingencies on IEEE 39-Bus Test System and compile the results in two categories of stable and unstable voltage cases. The second stage of the proposed work mainly deals with the study of different patterns of voltage using artificial neural networks. The final stage deals with the training of the controllers in order to detect stability of power system in advance."],"dc:identifier":["https://scholarworks.uno.edu/td/1279"],"dc:subject":["Real time simulator","Voltage stability","Voltage collapse","Blackout","Pattern recognition","Hypersim","EMTP"],"dc:title":["Pattern Recognition of Power Systems Voltage Stability Using Real Time Simulations"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T05:29:15Z"}