A Data Analytics Framework for Regional Voltage Control

dc.contributor.authorYang, Duotongen
dc.contributor.committeechairCenteno, Virgilio A.en
dc.contributor.committeememberThorp, James S.en
dc.contributor.committeememberDe La Ree, Jaimeen
dc.contributor.committeememberSouthward, Steve C.en
dc.contributor.committeememberTokekar, Pratapen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2017-08-17T08:00:33Zen
dc.date.available2017-08-17T08:00:33Zen
dc.date.issued2017-08-16en
dc.description.abstractModern power grids are some of the largest and most complex engineered systems. Due to economic competition and deregulation, the power systems are operated closer their security limit. When the system is operating under a heavy loading condition, the unstable voltage condition may cause a cascading outage. The voltage fluctuations are presently being further aggravated by the increasing integration of utility-scale renewable energy sources. In this regards, a fast response and reliable voltage control approach is indispensable. The continuing success of synchrophasor has ushered in new subdomains of power system applications for real-time situational awareness, online decision support, and offline system diagnostics. The primary objective of this dissertation is to develop a data analytic based framework for regional voltage control utilizing high-speed data streams delivered from synchronized phasor measurement units. The dissertation focuses on the following three studies: The first one is centered on the development of decision-tree based voltage security assessment and control. The second one proposes an adaptive decision tree scheme using online ensemble learning to update decision model in real time. A system network partition approach is introduced in the last study. The aim of this approach is to reduce the size of training sample database and the number of control candidates for each regional voltage controller. The methodologies proposed in this dissertation are evaluated based on an open source software framework.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:12343en
dc.identifier.urihttp://hdl.handle.net/10919/78712en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectRegional Voltage Controlen
dc.subjectData Analyticsen
dc.subjectDecision Treeen
dc.subjectOnline Boosting. Wide Area Measurementsen
dc.subjectData Miningen
dc.subjectNetwork Partitionen
dc.subjectMachine learningen
dc.titleA Data Analytics Framework for Regional Voltage Controlen
dc.typeDissertationen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en
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