November 23, 2021
Dear Faculty and Students,
You are cordially invited to my thesis defense.
Title: Transformer fault event detection and classification using PMUs
When: Thursday, December 9th, 2021, at 1:00 PM
Where: Simrall 228 (Conference Room)
Candidate: Yadunandan Paudel
Degree: Masters, Electrical and Computer Engineering
Dr. Yong Fu
Dr. Masoud Karimi
Dr. Bo Tang
Transformer is one of the most reliable components in an electric power system, however its failure has huge opportunity costs for an electric utility. In this work, we detect transformer electrical faults promptly and accurately classify the fault types using the voltage and current data obtained from Phasor Measurement Units. Our work can also eliminate the uncertainties which are inherent in traditional transformer fault diagnostic techniques like dissolved gas analysis. In this thesis, first, possible causes of transformer failures are discussed, and four common transformer electrical faults are identified. Second, a comprehensive simulation model for electrical faults is developed. Third, fast and efficient abrupt change detection algorithms are applied to detect the fault events. Finally, selected supervised machine learning classifiers are trained to classify the type of transformer electrical faults. Our proposed work can be used with alarms and relays to notify system operators and remove the faults.