Title:
An object-oriented distribution system distributed quasi-dynamic state estimator
An object-oriented distribution system distributed quasi-dynamic state estimator
Author(s)
Xie, Boqi
Advisor(s)
Meliopoulos, A. P. Sakis
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Abstract
This dissertation develops an object-oriented distribution system distributed quasi-dynamic state estimator (DS-DQDSE) that constantly monitors the states of the distribution system and provides the validated data to the control center. In particular, the dissertation presents a distributed and seamless infrastructure starting from measurement data from sensors installed across distribution systems to estimated states and system model output from the state estimator. To automate the whole procedure as well as to guarantee the accuracy of the output results from the state estimator, an object-oriented physically based high-fidelity device modeling approach is adopted. Given measurements and device models from a selected section, a network-level measurement model is created. The network-level measurement model is augmented with derived, pseudo, and virtual measurements to achieve observability and increase redundancy. The measurement model is then processed by the state estimator, which provides the best estimates of the monitored system and the confidence level that evaluates if the measurements are consistent with the system model. The output of the state estimator including estimated states, estimated measurements, and validated model of the monitored system are then transmitted to the control center where the states and the model of the whole system are synthesized. Since the designed DS-DQDSE adopts quasi-dynamic state estimation (QDSE) while conventional state estimator adopts static state estimation (SSE), a comparison study between QDSE and SSE is presented. Furthermore, the developed DS-DQDSE is applied on two real feeder models. The results show that the developed DS-DQDSE is applicable to the distribution systems with DER penetration.
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Date Issued
2020-07-28
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Resource Type
Text
Resource Subtype
Dissertation