The UTC Graduate School is pleased to announce that Jonathan Boyd will present Master’s research titled, AUTOMATED DETECTION AND PREDICTION OF ELECTRICAL DISTURBANCES IN A POWER TRANSMISSION SYSTEM on 03/10/2023 at 9:00 am in EMCS 426. Everyone is invited to attend.
Engineering
Chair: Donald R. Reising
Co-Chair:
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 work 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 work 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 project 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.