Graduation Semester and Year
Fall 2025
Language
English
Document Type
Thesis
Degree Name
Master of Science in Electrical Engineering
Department
Electrical Engineering
First Advisor
Dr. David Wetz
Abstract
Power quality disturbances (PQDs) are among the primary challenges facing modern electrical systems, as they degrade the performance and lifespan of connected equipment. This thesis investigates the relationship between the rate at which voltage waveform data are sampled, the reliability of these measurements, and the ability of deep neural networks to classify PQDs accurately. A one-dimensional convolutional neural network (CNN) was trained and evaluated across multiple sampling rates and signal-to-noise ratios to quantify how information loss in the temporal and spectral domains affects classification reliability. The results demonstrate that model accuracy degrades nonlinearly as sampling rate and signal-to-noise ratio (SNR) decrease, revealing information-theoretic limits that bound deep-learning performance in power quality (PQ) monitoring. These findings provide practical guidelines for optimizing data acquisition and signal fidelity in intelligent PQ systems.
Keywords
power quality, power quality event, power quality event classification, convolutional neural network, power quality standards, signal-to-noise ratio (SNR)
Disciplines
Power and Energy | Signal Processing
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Lloyd, Jordan D., "Power Quality Event Diagnosis Using Multi-Rate Neural Networks" (2025). Electrical Engineering Theses. 398.
https://mavmatrix.uta.edu/electricaleng_theses/398