Date of Award
2023
Degree Type
Dissertation
Degree Name
Doctor of Philosophy in Electrical Engineering
Department
Electrical, Computer, and Biomedical Engineering
First Advisor
Haibo He
Second Advisor
Yan Sun
Abstract
Power grid control centers have long relied on the legacy Supervisory Control and Data Acquisition (SCADA) system to protect and control the power grid. However, as the power grid moves towards the future Smart Grid, more sophisticated methods to maintain the resiliency of the critical infrastructure are required. The deployment of intelligent electronic devices, smart control algorithms and wide area monitoring systems has already begun on the modern power grid. The future Smart Grid offers many potential benefits but a large scale Cyber Physical System (CPS) also provides a large vulnerability surface with large repercussions. To address these issues, we propose a Cyber-Physical Digital Twin that has the ability to monitor and provide enhanced insight into the power system.
This research establishes a Digital Twin framework for Cyber Physical Systems using digital twin technology and state-of-the-art machine learning methods. While the research is focused on the Smart Grid as the representative CPS, this work includes the research of a Digital Twin framework along with novel technology modules that expand the Digital Twin landscape and provide resiliency to the physical system.
A key component of the proposed digital twin research is the Automatic Network Guardian for ELectrical systems (ANGEL) digital twin framework. This dissertation presents the research into ANGEL modules that can autonomous detect anomalies in the cyber-physical power system, improve the communication system performance, and provide additional system state information with limited access to sensor data. These methods were evaluated on a lab-based hardware in the loop system that was developed for real-time cyber-physical research and compared to the current state-of-the-art in the field.
Recommended Citation
Danilczyk, William C., "DIGITAL TWIN FOR INTELLIGENT CYBER-PHYSICAL SYSTEMS" (2023). Open Access Dissertations. Paper 1549.
https://digitalcommons.uri.edu/oa_diss/1549
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