Advancing Distribution Automation through Model-based and Machine Learning Approaches
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Author(s)
Chen, Zhengrong
Advisor(s)
Meliopoulos, A.P. Sakis
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Abstract
With the increasing number of distributed energy resources and electric vehicles, power systems, especially distribution systems, are transforming into active systems with renewable and low-carbon energy resources. Shifting from passive to active is one of the most significant characteristics of distribution systems, allowing bidirectional power flow from renewables. Such change brings challenges to distribution systems, including growing complexity, heightened uncertainty, frequent voltage violations, dynamic load demand, and cybersecurity issues. With the development of advanced metering infrastructures, leveraging extensive sampled and historical data enables real-time monitoring and control to enhance the resilience of active distribution networks under uncertainties and cyberattacks. This dissertation aims to advance distribution automation in terms of protection, control, and optimization to ensure secure, reliable, and resilient distribution system operation. Specifically, this dissertation explores advanced model-based and data-driven methodologies in this framework, including state estimation, fault diagnosis, voltage control, and load management.
The main contributions of this dissertation include: (1) the design of the distribution automation platform, which utilizes smart meter data for distribution system applications to enhance resilience; (2) the development of the real-time fault diagnosis framework with high accuracy and robustness; (3) the innovation of the robust deep reinforcement learning method to solve optimization problem under uncertainties; (4) the formulation and mitigation methodology for pricing integrity attacks on the energy market.
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Date
2024-12-07
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Dissertation