Term of Award

Summer 2024

Degree Name

Master of Science, Electrical Engineering

Document Type and Release Option

Thesis (restricted to Georgia Southern)

Copyright Statement / License for Reuse

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

Department

Department of Electrical and Computer Engineering

Committee Chair

Masoud Davari

Committee Member 1

Rocio Alba-Flores

Committee Member 2

Fernando Rios-Gutierrez

Abstract

The implementation of renewable energy and decentralization of power generation over the last years created the need for superior technology to be implemented into the grid, which had to transform and integrate these variable renewable energy sources into the existing grid. This technology aims to completely replace the traditional technology in the grid by providing more efficient electricity generation, transmission, and distribution, as well as ensuring benefits such as high reliability, lower cost, higher efficiency, and more compactness, among others. However, the inclusion of power electronic converters into the grid brings many challenges, such as harmonic distortions introduced by power electronic converters and non-linear operation. In addition, power electronic converters can bring challenges related to the quality of the power generated, as well as the stability of the system. In other words, converter design will be critical depending on its application. The most optimum design can be done after a thorough study of the behavior of the converts as well as how to optimize its characteristics. Therefore, this work develops the modeling process of the reliability of power electronic converters at the component, sub-system, and power system level and the optimization of the proposed reliability improvement model. This optimization will be done by using different computational optimization techniques, also called metaheuristic algorithms, such as the Harris Hawk Optimization (HHO) algorithm, the Artificial Bee Colony (ABC) algorithm, and the Particle Swarm Optimization (PSO) algorithm. The objective is to compare each algorithm’s performance in terms of convergence rate and the fitness of the parameters in terms of constraints provided by the designer.

OCLC Number

1450369268

Research Data and Supplementary Material

Yes

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