Committee Chair

Reising, Donald R.

Committee Member

Disfani, Vahid R.; Karrar, Abdelrahman

Department

Dept. of Electrical Engineering

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

As power quality becomes a higher priority in the electric utility industry, utilities simply do not have the required personnel to analyze the ever-growing amount of data by hand. This thesis presents an automated approach for the analysis of power quality phenomena within a power transmission system by leveraging rule-based analytics as well as machine learning to analyze the characteristics of the recorded data. Waveform signatures analyzed within this thesis include: various faults, motor starting, and incipient instrument transformer failure. The developed analytics were tested on 160 waveform files and yielded an average accuracy of 99%. Machine learning techniques are also used to predict voltage unbalance on the transmission system above a certain threshold, which yielded an accuracy of over 91%. This work will result in time savings for engineers as well as increased reliability of the transmission system by providing near real-time detection, identification, and prevention of disturbances.

Acknowledgments

I would first like to thank Dr. Donald Reising, chair of my committee, for the direction, guidance, and expertise that he provided throughout the course of this project. Thanks also to the other members of my committee, Dr. Abdelrahman Karrar and Dr. Vahid Disfani, for their time and willingness to serve on my committee. I would also like to extend a special thanks to Mr. Tony Murphy of the Tennessee Valley Authority (TVA) for his continual guidance during this whole project. His knowledge and expertise in the Power Quality area proved to be invaluable during the course of this research. Thanks also to the other engineers at TVA who assisted at different points along the way, including Mr. Nathan Hooker, Mr. Justin Kuhlers, Mr. Michael McAmis, and Mr. Jim Rossman. This project was completed as part of a research grant from the Tennessee Valley Authority awarded to the University of Tennessee at Chattanooga. Thanks to both my parents as well as my fiancé, Lesley, for their encouragement and support throughout this endeavor. Finally, I would like to give thanks to God for blessing me with this opportunity to learn and grow in knowledge, and for the ability and endurance to complete this project.

Degree

M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.

Date

5-2023

Subject

Electric power system stability; Power transmission; Machine learning

Keyword

Electrical Disturbance; Power Quality; Voltage Unbalance; Machine Learning

Document Type

Masters theses

DCMI Type

Text

Extent

xi, 67 leaves

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/3.0/

Date Available

5-1-2024

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