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
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Electrical Engineering
Committee Chair
Reza Hamidi
Committee Member 1
Mohammad Ahad
Committee Member 2
Fernando Rios
Abstract
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.
Recommended Citation
Bhadra, Ananta Bijoy, "Machine Learning Based Three-Limb Core-Type Transformer Core Aspect Ratios Identification" (2024). Electronic Theses and Dissertations. 2803.
https://digitalcommons.georgiasouthern.edu/etd/2803
Research Data and Supplementary Material
No
