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dc.contributor.authorDelport, Jacquesen_US
dc.date.accessioned2018-06-02T08:00:18Z
dc.date.available2018-06-02T08:00:18Z
dc.date.issued2018-06-01
dc.identifier.othervt_gsexam:15650en_US
dc.identifier.urihttp://hdl.handle.net/10919/83444
dc.description.abstractSubstations are joints in the power system that represent nodes that are vital to stable and reliable operation of the power system. They contrast the rest of the power system in that they are a dense combination of critical components causing all of them to be simultaneously vulnerable to one isolated incident: weather, attack, or other common failure modes. Undoubtedly, the loss of these vital links will have a severe impact to the to the power grid to varying degrees. This work creates a cascading model based on protection system misoperations to estimate system risk from loss-of-substation events in order to assess each substation's criticality. A continuation power flow method is utilized for estimating voltage collapse during cascades. Transient stability is included through the use of a supervised machine learning algorithm called random forests. These forests allow for fast, robust and accurate prediction of transient stability during loss-of-substation initiated cascades. Substation risk indices are incorporated into a preventative optimal power flow (OPF) to reduce the risk of critical substations. This risk-based dispatch represents an easily scalable, robust algorithm for reducing risk associated with substation losses. This new dispatch allows operators to operate at a higher cost operating point for short periods in which substations may likely be lost, such as large weather events, likely attacks, etc. and significantly reduce system risk associated with those losses. System risk is then studied considering the interaction of a power grid utility trying to protect their critical substations under a constrained budget and a potential attacker with insider information on critical substations. This is studied under a zero-sum game theoretic framework in which the utility is trying to confuse the attacker. A model is then developed to analyze how a utility may create a robust strategy of protection that cannot be heavily exploited while taking advantage of any mistakes potential attackers may make.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectcritical substationsen_US
dc.subjectsubstation risken_US
dc.subjectsystem risken_US
dc.subjectcascadingen_US
dc.subjecthidden failuresen_US
dc.subjectmisoperationsen_US
dc.subjectimportance samplingen_US
dc.subjectstability predictionen_US
dc.subjectoptimal power flowen_US
dc.subjectgame theoryen_US
dc.subjectrestricted nash responseen_US
dc.subjectexploitationen_US
dc.subjectexploitabilityen_US
dc.titleCritical Substation Risk Assessment and Mitigationen_US
dc.typeDissertationen_US
dc.contributor.departmentElectrical Engineeringen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical Engineeringen_US
dc.contributor.committeechairCenteno, Virgilio A.en_US
dc.contributor.committeememberAbbott, Amos L.en_US
dc.contributor.committeememberPhadke, Arun G.en_US
dc.contributor.committeememberDe La Reelopez, Jaimeen_US
dc.contributor.committeememberMarathe, Madhav Vishnuen_US
dc.contributor.committeememberBernabeu, Emanuel Ernestoen_US
dc.contributor.committeememberThorp, James S.en_US


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