Doctoral Dissertations
Date of Award
8-2025
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Major
Electrical Engineering
Major Professor
Yilu Liu
Committee Members
Fangxing (Fran) Li, Lin Zhu, Yi Zhao
Abstract
The presence of forced oscillations and poorly damped low-frequency oscillations poses significant challenges to the operation of modern interconnected power systems. These phenomena can lead to severe consequences, including damage to critical equipment, reduced efficiency in power transfer, and compromised overall stability of the grid. Forced oscillations, often triggered by periodic disturbances, propagate through networks, amplifying stress on system components. Similarly, low-frequency oscillations, arising from weak system damping, can persist and undermine operational reliability. Addressing these challenges is critical to maintaining the safety and resilience of power systems amid increasing complexity and demand. The first part of this dissertation investigates how different exciter and governor model parameters influence the magnitude of forced oscillations. Chapter 2 examines these parameter impacts, providing insights for power system planners and operators to optimize settings and enhance stability. Chapter 3 explores the forced oscillation frequency impact under resonance conditions. Chapter 4 categorizes oscillation sources, identifying exciters, governors, or renewable energy plants as potential origins. Mitigation strategies using inverter-based resource (IBR) actuators are explored in Chapter 5. The second part addresses the growing complexity of modern power grids, intensified by the integration of intermittent renewable energy sources. Simulating large-scale systems using traditional model-based approaches has become computationally demanding. To overcome this, a measurement-based model reduction approach utilizing vii long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks is proposed. This technique enables efficient and accurate analysis of power system dynamics, offering a scalable solution for managing the challenges posed by renewable integration. This comprehensive dissertation provides a multi-faceted approach to improving power system stability and resilience, offering practical solutions for managing forced oscillations, leveraging advanced machine learning techniques for system analysis.
Recommended Citation
Thotakura, Naga, "Forced Oscillation Analysis and Model Reduction Techniques in Power Systems. " PhD diss., University of Tennessee, 2025.
https://trace.tennessee.edu/utk_graddiss/12666