Data-Driven Analysis and Topology-Aware Learning of Phasor and Waveform Measurements for Enhanced Situational Awareness in Power Systems
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Authors
Mansourlakouraj, Mohammad
Issue Date
2024
Type
Dissertation
Language
en_US
Keywords
Data-Driven , Event Detection Classification and Localization , PMU , Power System , Topology-Aware Learning , Waveform Measurement
Alternative Title
Abstract
Power grids are evolving with the integration of more renewable generation resources and different types of loads. This shift introduces new types of challenging events, oscillations, and controller responses, in addition to typical faults and outages. Additionally, phasor and waveform measurement devices are being increasingly used, measuring system variables such as voltage and current at a high reporting rate across the grid. This provides opportunities to enhance modern power grid monitoring during events and grid responses. Therefore, a fundamental question is how to analyze this valuable recorded data for practical power system monitoring and achieve better situational awareness. This dissertation is concerned with data-driven analysis of events and operational changes of assets by using statistical analysis, signal processing, and topology-aware learning methods. Events, abrupt changes and oscillations in power systems create specific signatures on the measurement signals, so effectively analyzing them helps enhance event detection, clustering, classification, and localization of their sources. These attempts, relying on the proposed methods in this dissertation, such as graph-based learning, short-time modal analysis, and statistical wavelet-based studies, can provide energy utilities with insight into the ongoing conditions in the power system. This can enable them to take proper actions, predict grid responses, and prevent failures at early stages, ensuring reliable and sustainable energy for people.
Description
Citation
Publisher
License
CC BY