Theses and Dissertations Collection

As the capacity of power systems worldwide is growing, the need for accurate equipment models to keep the grid stable has increased. When installing new generation equipment, nameplate data will be provided by the contractors to be recorded and documented. Currently, there is a shift to include as much renewable energy generation sources as possible. However, to incorporate these facilities into the modern power system, detailed studies are needed to realize benefits of the new generation. Due to the need for an accurate model of the power system, the WECC and NERC introduced a new policy which states that utilities need to maintain up to date parameters for their systems. Consequently, the common practice is to follow the IEEE 115 standard which requires that the generator is taken offline. Taking a generator offline creates an enormous cost for the generation facility due to the time required to parameterize the machines, so revenue is lost.

To regain revenue where possible, methods to parameterize machine while they are online have been explored. This thesis explores algorithms and advises ways to parameterize a generator while it is online. The Joint-Unscented Kalman Filter was chosen to capture the states and estimate the parameters of a salient pole synchronous machine while it is online. The chosen filter algorithm was tested, tuned, and verified against a simulated generator. Furthermore, the Joint-Unscented Kalman filter algorithm was tested on a physical laboratory setup and was compared against IEEE 115 tests conducted on the machine, providing promising results toward further industrial implementation.