Electrical and Computer Engineering ETDs

Publication Date

Spring 5-11-2026

Abstract

In DC power systems, rapid fault location is crucial for maintaining reliable operation, particularly with the prevalence of DC-DC converters. This study investigates fault location techniques in DC systems utilizing Traveling Waves (TWs). Following data normalization, multi-resolution analysis employs discrete wavelet transform to capture high-frequency patterns of TW's wavelet coefficients. Parseval's theorem is utilized to quantify the energy of these coefficients. First, a curve-fitting technique is employed to estimate fault locations in DC microgrids. Then, two transfer learning approaches are proposed: first approach integrates Parseval energy curves into a Gaussian process estimator, while second employs feedforward neural network for fault prediction. Hardware implementation of TW protection device is also explored, involving real-world testing and validation in the Emera Technologies Kirtland Airforce Base low-voltage DC microgrid. Through experimental validation and field tests, the effectiveness of the proposed methodologies in achieving fast and accurate fault location in DC power systems is demonstrated.

Keywords

Power system protection, DC microgrid, Fault location, Discrete wavelet transform, Traveling wave, Parseval energy, Multi-resolution analysis, Machine learning, Hardware implementation.

Document Type

Dissertation

Language

English

Degree Name

Electrical Engineering

Level of Degree

Doctoral

Department Name

Electrical and Computer Engineering

First Committee Member (Chair)

Ali bidram

Second Committee Member

Manel Martinez-Ramon

Third Committee Member

Ramiro Jordan

Fourth Committee Member

Sumit Paudyal

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Available for download on Tuesday, May 12, 2026

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