Date of Award

5-2011

Embargo Period

10-11-2011

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical and Computer Engineering

Advisor(s)

Bruce Krogh

Second Advisor

Marija Ilic

Abstract

This thesis addresses the challenge of accurately and robustly estimating the network state on an electric power network despite its large size, infrequent measurement updates, and high likelihood of corrupted data. This is especially important as electrical transmission operators are increasingly being asked to operate the networks at their maximum allowable capacity. Accurate knowledge of the state is necessary to ensure adequate margin to these operating limits should a fault occur.

This thesis provides the following contributions. 1. Models describing the dynamics of slow machinery attached to and coupled via the electric power network were used to allow dynamic state estimation. 2. The detail of the coupled dynamic network model was evaluated to determine the level of modeling complexity required to achieve significant state estimation performance gains. 3. Improvements to bad data detection and identification by using information from the dynamic state estimator were demonstrated and evaluated. 4. The improvements to network static observability were discussed and evaluated.

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