678617 event 1732453543 1732453603 <![CDATA[Ph.D. Dissertation Defense - Zhengrong Chen]]> Title:  Advancing Distribution Automation through Model-based and Machine Learning Approaches

Committee:

Dr. Sakis Meliopoulos, ECE, Chair, Advisor

Dr. Santiago Grijalva, ECE

Dr. Daniel Molzahn, ECE

Dr. Ying Zhang, ECE

Dr. Constance Crozier, ISyE

]]> With the rise in distributed energy resources (DERs) and electric vehicles (EVs), electric 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 DERs. 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 (AMI), leveraging extensive sampled and historical data enables real-time monitoring and control to enhance the resilience of Active Distribution Networks (ADNs) under uncertainties and cyberattacks. This dissertation aims to advance distribution automation (DA) 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.

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