A New State Transition Model for Forecasting-Aided State Estimation for the Grid of the Future

dc.contributor.authorHassanzadeh, Mohammadtaghien
dc.contributor.committeechairEvrenosoglu, Cansin Yamanen
dc.contributor.committeememberCenteno, Virgilio A.en
dc.contributor.committeememberMili, Lamine M.en
dc.contributor.committeememberBaumann, William T.en
dc.contributor.committeememberDe La Ree, Jaimeen
dc.contributor.committeememberde Sturler, Ericen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2016-01-01T07:00:18Zen
dc.date.available2016-01-01T07:00:18Zen
dc.date.issued2014-07-09en
dc.description.abstractThe grid of the future will be more decentralized due to the significant increase in distributed generation, and microgrids. In addition, due to the proliferation of large-scale intermittent wind power, the randomness in power system state will increase to unprecedented levels. This dissertation proposes a new state transition model for power system forecasting-aided state estimation, which aims at capturing the increasing stochastic nature in the states of the grid of the future. The proposed state forecasting model is based on time-series modeling of filtered system states and it takes spatial correlation among the states into account. Once the states with high spatial correlation are identified, the time-series models are developed to capture the dependency of voltages and angles in time and among each other. The temporal correlation in power system states (i.e. voltage angles and magnitudes) is modeled by using autoregression, while the spatial correlation among the system states (i.e. voltage angles) is modeled using vector autoregression. Simulation results show significant improvement in power system state forecasting accuracy especially in presence of distributed generation and microgrids.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:3050en
dc.identifier.urihttp://hdl.handle.net/10919/64407en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectState transition modelen
dc.subjectforecasting-aided state estimationen
dc.subjecttime-series analysisen
dc.subjectvector autoregressionen
dc.titleA New State Transition Model for Forecasting-Aided State Estimation for the Grid of the Futureen
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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