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

The 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.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Electrical Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2014

Author dc:creator

• Hassanzadeh, Mohammadtaghi

Chair dc:contributor.committeechair

• Evrenosoglu, Cansin Yaman

Committee members dc:contributor.committeemember

• Centeno, Virgilio A.
• Mili, Lamine M.
• Baumann, William T.
• De La Ree, Jaime
• de Sturler, Eric

• State transition model
• forecasting-aided state estimation
• time-series analysis
• vector autoregression

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:3050

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/64407