Dixit, Vishal Sateesh2024-06-132024-06-132024-06-12vt_gsexam:40940https://hdl.handle.net/10919/119423With the proliferation of renewable energy and its integration into the modern power grid, we face some new issues. Aside from the increased switching rate, which results in faster dynamic behavior, realistic models for these Inverter-Based Resources (IBRs) are not widely available. This complicates researching the behavior of this quickly changing system, and without proper models, simulations may not be totally reliable. To address this, it is recommended that measurement data be used, which includes the entire grid and all of its unique characteristics. Signal processing techniques have been employed exclusively to construct spectrograms, which are time-frequency representations of a signal's spectral information. These spectrograms show ridges that represent the system's changing modes. It can be extremely beneficial to track these modes and generate labeled data representing the evolution of modes as the system evolves. This labeled data can aid in the development of correlation and causation hypotheses linking specific abnormal behaviors to proximity to instability. This can also assist analyze these IBRs and identify flaws in their modeling. This thesis describes a step-by-step process for creating spectrograms, reducing them for better visualization, and then estimating mode evolution with a ridge-tracking algorithm based on penalized jump criteria. The results show that the tracker works effectively with both synthetic and real PMU data.ETDenIn CopyrightSpectrogramAmbient dataTime-frequencyspectral analysissynchrophasorsThesis