{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/78805"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/78805","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Hoeffding-Tree-Based Learning from Data Streams and Its Application in Online Voltage Security Assessment","abstract":"According to the proposed definition and classification of power system stability addressed by IEEE and CIGRE Task Force, voltage stability refers to the stability of maintaining the steady voltage magnitudes at all buses in a power system when the system is subjected to a disturbance from a given operating condition (OC). Cascading outage due to voltage collapse is a probable consequence during insecure voltage situations. In this regard, fast responding and reliable voltage security assessment (VSA) is effective and indispensable for system to survive in conceivable contingencies. This paper aims at establishing an online systematic framework for voltage security assessment with high-speed data streams from synchrophasors and phasor data concentrators (PDCs). Periodically updated decision trees (DTs) have been applied in different subjects of security assessments in power systems. However, with a training data set of operating conditions that grows rapidly, re-training and restructuring a decision tree becomes a time-consuming process. Hoeffding-tree-based method constructs a learner that is capable of memory management to process streaming data without retaining the complete data set for training purposes in real-time and guarantees the accuracy of learner. The proposed approach of voltage security assessment based on Very Fast Decision Tree (VFDT) system is tested and evaluated by the IEEE 118-bus standard system.","abstract_html":"According to the proposed definition and classification of power system stability addressed by IEEE and CIGRE Task Force, voltage stability refers to the stability of maintaining the steady voltage magnitudes at all buses in a power system when the system is subjected to a disturbance from a given operating condition (OC). Cascading outage due to voltage collapse is a probable consequence during insecure voltage situations. In this regard, fast responding and reliable voltage security assessment (VSA) is effective and indispensable for system to survive in conceivable contingencies. This paper aims at establishing an online systematic framework for voltage security assessment with high-speed data streams from synchrophasors and phasor data concentrators (PDCs). Periodically updated decision trees (DTs) have been applied in different subjects of security assessments in power systems. However, with a training data set of operating conditions that grows rapidly, re-training and restructuring a decision tree becomes a time-consuming process. Hoeffding-tree-based method constructs a learner that is capable of memory management to process streaming data without retaining the complete data set for training purposes in real-time and guarantees the accuracy of learner. The proposed approach of voltage security assessment based on Very Fast Decision Tree (VFDT) system is tested and evaluated by the IEEE 118-bus standard system.","abstract_has_math":false,"creators":["Nie, Zhijie"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Centeno, Virgilio A."],"committee_members":["Kekatos, Vasileios","De La Ree, Jaime"],"year":2017,"date_issued":"2017-09-05","date_published":"2017-09-05","updated_at":"2026-07-22T22:18:47Z","subjects":["Power Systems Stability","Voltage Security Assessment","Machine Learning","Decision Trees","Hoeffding Trees"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:12647"],"render_values":[{"text":"vt_gsexam:12647","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/78805","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Centeno, Virgilio A."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Kekatos, Vasileios","De La Ree, Jaime"]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Nie, Zhijie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-09-06T08:00:59Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-09-06T08:00:59Z"]},{"key":"dc:date.issued","label":"Date","values":["2017-09-05"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Power Systems Stability","Voltage Security Assessment","Machine Learning","Decision Trees","Hoeffding Trees"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:12647"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/78805"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["According to the proposed definition and classification of power system stability addressed by IEEE and CIGRE Task Force, voltage stability refers to the stability of maintaining the steady voltage magnitudes at all buses in a power system when the system is subjected to a disturbance from a given operating condition (OC). Cascading outage due to voltage collapse is a probable consequence during insecure voltage situations. In this regard, fast responding and reliable voltage security assessment (VSA) is effective and indispensable for system to survive in conceivable contingencies. This paper aims at establishing an online systematic framework for voltage security assessment with high-speed data streams from synchrophasors and phasor data concentrators (PDCs). Periodically updated decision trees (DTs) have been applied in different subjects of security assessments in power systems. However, with a training data set of operating conditions that grows rapidly, re-training and restructuring a decision tree becomes a time-consuming process. Hoeffding-tree-based method constructs a learner that is capable of memory management to process streaming data without retaining the complete data set for training purposes in real-time and guarantees the accuracy of learner. The proposed approach of voltage security assessment based on Very Fast Decision Tree (VFDT) system is tested and evaluated by the IEEE 118-bus standard system."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Voltage security is one of the most critical issues in the power systems operation. Given an operating condition (OC), Voltage Security Assessment (VSA) provides a tool to access whether the system is capable to withstand disturbances if there is one or more than one elements is not functioning appropriately on the power grid. Traditional methods of VSA require the knowledge of network topologies and the computational contingency analysis of various circumstances. With trained models, decision-tree-based VSA is able to assess the voltage security status by collectible measurements among the system in a real-time manner. The system topology may alter over and over by system operators in order to meet the needs of heavy load demand and power quality requirements. The proposed approach based on Very Fast Decision Tree (VFDT) system is capable of updating trained decision-tree models regarding to changes of system topology. Therefore, the updated decision-tree models is able to handle different system topology and to provide accurate security assessment of current OC again."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Hoeffding-Tree-Based Learning from Data Streams and Its Application in Online Voltage Security Assessment"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Centeno, Virgilio A."],"dc:contributor.committeemember":["Kekatos, Vasileios","De La Ree, Jaime"],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Nie, Zhijie"],"dc:date.accessioned":["2017-09-06T08:00:59Z"],"dc:date.available":["2017-09-06T08:00:59Z"],"dc:date.issued":["2017-09-05"],"dc:description.abstract":["According to the proposed definition and classification of power system stability addressed by IEEE and CIGRE Task Force, voltage stability refers to the stability of maintaining the steady voltage magnitudes at all buses in a power system when the system is subjected to a disturbance from a given operating condition (OC). Cascading outage due to voltage collapse is a probable consequence during insecure voltage situations. In this regard, fast responding and reliable voltage security assessment (VSA) is effective and indispensable for system to survive in conceivable contingencies. This paper aims at establishing an online systematic framework for voltage security assessment with high-speed data streams from synchrophasors and phasor data concentrators (PDCs). Periodically updated decision trees (DTs) have been applied in different subjects of security assessments in power systems. However, with a training data set of operating conditions that grows rapidly, re-training and restructuring a decision tree becomes a time-consuming process. Hoeffding-tree-based method constructs a learner that is capable of memory management to process streaming data without retaining the complete data set for training purposes in real-time and guarantees the accuracy of learner. The proposed approach of voltage security assessment based on Very Fast Decision Tree (VFDT) system is tested and evaluated by the IEEE 118-bus standard system."],"dc:description.abstractgeneral":["Voltage security is one of the most critical issues in the power systems operation. Given an operating condition (OC), Voltage Security Assessment (VSA) provides a tool to access whether the system is capable to withstand disturbances if there is one or more than one elements is not functioning appropriately on the power grid. Traditional methods of VSA require the knowledge of network topologies and the computational contingency analysis of various circumstances. With trained models, decision-tree-based VSA is able to assess the voltage security status by collectible measurements among the system in a real-time manner. The system topology may alter over and over by system operators in order to meet the needs of heavy load demand and power quality requirements. The proposed approach based on Very Fast Decision Tree (VFDT) system is capable of updating trained decision-tree models regarding to changes of system topology. Therefore, the updated decision-tree models is able to handle different system topology and to provide accurate security assessment of current OC again."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:12647"],"dc:identifier.uri":["http://hdl.handle.net/10919/78805"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Power Systems Stability","Voltage Security Assessment","Machine Learning","Decision Trees","Hoeffding Trees"],"dc:title":["Hoeffding-Tree-Based Learning from Data Streams and Its Application in Online Voltage Security Assessment"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:18:47Z"}