Date of Graduation

12-2015

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Engineering (PhD)

Degree Level

Graduate

Department

Electrical Engineering

Advisor/Mentor

McCann, Roy A.

Committee Member

Balda, Juan C.

Second Committee Member

Martin, Terry

Third Committee Member

Ang, Simon S.

Fourth Committee Member

Thompson, Dale R.

Keywords

Applied sciences; Model validation; Power system modeling; Power systems; Synchrophasors; Ybus estimation

Abstract

In this research a new benchmark system is proposed for wind energy transmission systems. New model development, validation, and calibration methods for power transmission systems are proposed and implemented as well. First, a model reduction criteria is chosen based on electrical interconnection and geographical information. Model development is then done using reduction techniques on an operation model provided by a transmission operator based on the chosen criteria. Then model validation is performed using actual PMU synchrophasor measurements provided by a utility company. The model development and validation process ensures the accuracy of the developed model and makes for a realistic benchmark system for wind generation transmission systems. The new proposed model development and validation methods are generic and can be used to model any power transmission system for various simulation needs. Nevertheless, the accuracy of the benchmark model is constrained by the accuracy of the initial operational model. In this research, a new parameter estimation technique for determining the bus admittance matrix (Ybus) is also proposed to further calibrate power system models. Ybus estimation is done using recorded PMU synchrophasor measurements. The approach proposed in this research is based on recognizing that bus injection currents Ibus can be viewed as signals produced by a random process. In this manner, the corresponding bus voltages Vbus are also stochastic signals that are related through a cross-covariance matrix to the vector Ibus. Using estimation techniques developed for statistical signal processing, the cross-covariance matrix is shown to be Zbus.

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