| Summary: | For decades, researchers have sought to make the North American power system as
reliable as possible with many security measures in place to include redundancy. Yet the
increasing number of blackouts and failures have highlighted the areas that require
improvement. Meeting the increasing demand for energy and the growing complexity of the
loads are two of the main challenges faced by the power grid. In order to prepare for
contingencies and maintain a secure state, power engineers must perform simulations using
steady state and dynamic models of the system. The results from the contingency studies are
only as accurate as the models of the grid components. The load components are generally the most difficult to model since they are controlled by the consumer. This study focuses on
developing static and dynamic load models using advanced mathematical approximation
algorithms and wide area measurement devices, which will improve the accuracy of the system
analysis and hopefully decrease the frequency of blackouts.
The increasing integration of phasor measurement units (PMUs) into the power system
allows us to take advantage of synchronized measurements at a high data rate. These devices
are capable of changing the way we manage online security within the Energy Management
System (EMS) and can enhance our offline tools. This type of data helps us redevelop the
measurement-based approach to load modeling.
The static ZIP load model composition is estimated using a variation of the method of
least squares, called bounded-variable least squares. The bound on the ZIP load parameters
allows the measurement matrix to be slightly correlated. The ZIP model can be determined
within a small range of error that won't affect the contingency studies. Machine learning is used
to design the dynamic load model. Neural network training is applied to fault data obtained
near the load bus and the derived network model can estimate the load parameters. The neural network is trained using simulated data and then applied to real PMU measurements. A PMU algorithm was developed to transform the simulated measurements into a realistic
representation of phasor data. These new algorithms will allow us to estimate the load models
that are used in contingency studies. === Ph. D.
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