Paper Type

Master's Thesis


College of Computing, Engineering & Construction

Degree Name

Master of Science in Electrical Engineering (MSEE)



NACO controlled Corporate Body

University of North Florida. School of Engineering

First Advisor

Dr. Touria El Mezyani

Second Advisor

Dr. Mona Nasseri


This thesis covers the increasing usage of machine learning techniques in the field of power electronics. These uses are broad and efficient, but not without flaws. The major flaw being the requirement of time to collect data if there is not pre-existing datasets available. To combat this, a hybrid combination of existing physics-based modeling and data-driven modeling is proposed to limit the high computational strain of physics-models while simultaneously preventing the need for large pre-existing datasets. This method is then simulated and tested on a buck-converter with several disturbances applied to it to stimulate non-linear dynamics known as bifurcations that cannot be easily predicted by physics-based methods. The results show that hybrid methodology is more efficient and robust than data-driven methods alone and can efficiently predict the output of a system even when under the effect of bifurcations.

Available for download on Monday, May 08, 2028