Data-Driven Analysis and Dynamic Modeling of Inverter-Based Resources Under Grid Disturbances Enabled by Automated Event Region Identification
The increasing penetration of solar power generation continues to pose new challenges to the power grid; with respect to planning studies, power quality, system control, and operation. In an effort to address some of these challenges, this doctoral thesis focuses of two aspects: 1) improving the self-aware capabilities of smart inverters in solar farms, and 2) analysis of solar farm's behavior and modeling its dynamic response to the grid disturbances. Research is done in three main stages to accomplish these objectives.
First, we capture power system events at solar farms, benefiting from the diagnostic application of distribution phasor measurement units (micro-PMUs) installed in the feeder head of a behind-the-meter solar farm.We determine the source region for each event, and analyze different types of events. According to the event's region, we either examine the impact of solar production level and other significant parameters to make statistical conclusions or we characterize the response of the solar farm. Our results reveal the smart inverter behavior by revealing the dynamics to the control system of the solar distribution feeder.
Second, we locate the region of the events with automatic approaches and build a foundation for event-based situational awareness and its data-driven applications. This provides knowledge of the system for not only the grid operators but also for the smart inverters to help with their self awareness to understand the impact of their operation on the power grid. Several examples of the applications of our methodology is presented. Third, during and after grid disturbances, we model the dynamic response of the solar farm to the events that occur in the power grid. Accordingly, we predict the impact of the grid disturbance on the real and reactive power injections of smart inverters.