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Enhanced Situational Awareness in Power Distribution Systems: Data-Driven Analysis of Harmonic Synchro-Phasors and Synchro-Waveforms
- Ahmadi Gorjayi, Fatemeh
- Advisor(s): Mohsenian-Rad, Hamed
Abstract
Harmonic Phasor Measurement Units (H-PMUs) and Waveform Measurement Units (WMUs) are two emerging smart grid sensor technologies that are critical for enhancing situational awareness in power distribution systems and distributed inverter-based resources (IBRs), especially in the era of increased reliance on renewable energy resources and advanced grid edge technologies.H-PMUs can particularly assist power system operators to enhance reliability in monitoring harmonic distortions, which have become more prevalent due to the growing number of power electronic devices and inverter-based energy resources. Harmonic State Estimation (HSE) plays a key role in developing real-time monitoring systems to help power distribution system operators identify harmonic sources and track their propagation. However, the limited deployment of power quality sensors in real-world power system presents a significant challenge. To tackle this open problem, this thesis develops novel physics-aware HSE methods that leverage the radial topology of power distribution systems to identify sparsity patterns. The two proposed approaches are: a Physics-Aware Sparse HSE using constrained weighted-Lasso optimization for single harmonic sources, and a Physics-Aware Mixed Integer Quadratic Program (MIQP) to estimate the number and locations of multiple harmonic sources without prior information. Additionally, this thesis examines the information content in harmonic phasor signatures from power system events. Using real-world H-PMU measurements, we demonstrate that harmonic phasors can reveal new information content about power system events that are not captured by fundamental phasors from conventional PMUs. The proposed data-driven, information-theoretic approach enhances event clustering, particularly for transient and high-frequency events. Furthermore, this thesis addresses the challenges due to the increasing deployment of Distributed Energy Resources (DERs) and their impact on the dynamic behavior of power systems, often occurring within fractions of a cycle. Utilizing real-world data from WMUs, novel data-driven methods were proposed to model the dynamic response of IBRs to high-frequency disturbances. These methods operate in both frequency domain, such as using modal analysis techniques, and in time domain, employing regression techniques such as finite impulse response and auto-regressive eXogenous models, to estimate IBRs’ responses to high-frequency disturbances.
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