Term of Award

Summer 2024

Degree Name

Master of Science, Electrical Engineering

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


Department of Electrical Engineering

Committee Chair

Reza Hamidi

Committee Member 1

Mohammad Ahad

Committee Member 2

Fernando Rios


Power transformers are considered one of the key elements of electric grids. Transient studies include transformer transient analysis which is required for the continuous power supply. However, to perform the transient analysis, the details of the internal structure of the transformer are required which are unobtainable and considered as confidential information. Therefore, the application of topological-based transformer models is limited although the models can accurately represent the transformers. To address this concern, a novel approach utilizing Machine Learning (ML) to identify the core aspect ratios of the three-limb core-type transformer is introduced. The proposed approach, using only the voltage and current measurements in the steady-state, no-load condition, employs the Extreme Gradient boosting (XGBoost) algorithm to identify the core aspect ratios. MATLAB/Simscape is used to model transformers. The results illustrate that the proposed algorithm is able to identify the core aspect ratios correctly.

OCLC Number


Research Data and Supplementary Material