Term of Award

Summer 2022

Degree Name

Master of Science, Electrical Engineering

Document Type and Release Option

Thesis (restricted to Georgia Southern)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Electrical Engineering

Committee Chair

Masoud Davari

Committee Member 1

Rocio Alba-Flores

Committee Member 2

Fernando Rios-Gutierrez

Abstract

The modern power grid has become increasingly dependent on wireless communications to fulfill its duties. These new, more automatic, hands-off grids have become cyber-physical power systems. This more recent, more decentralized power grid has created profound changes in how power is generated and distributed. Moreover, to better accommodate these newer technologies inside the older legacy grid, more devices that can connect to or interact with the Internet have been required for sensor and actuator information to be collected, transmitted, and then processed. These devices have created what is known as the Internet of Things (IoT) in the power stations. This creates a more efficient and consistent power grid with less need for human assistance or interference. Of course, the increased utilization of cyber-physical power systems in the modern world has created an increasing concern about what would happen should some person or group stage a cyber-attack on the power grid. In that vein, there is much to identify and quantify the vulnerabilities associated with Supervisory Control and Data Acquisition (SCADA) and Industrial Control System (ICS) devices. It’s still primarily believed that current systems are vulnerable and need improvements as they may suffer from the same issues as the existing systems. This thesis will provide a brief overview of the threats to the power grid and how they are currently being combated. In addition, a potential machine learning neural network-based anomaly detection will be discussed.

OCLC Number

1362884139

Research Data and Supplementary Material

No

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