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 and Computer Engineering

Committee Chair

Masoud Davari

Committee Member 1

Rocio Alba-Flores

Committee Member 2

Fernando Rios


Power electronic converter (PEC) systems are vital for efficient and reliable electrical energy conversion across various applications. Since the overall reliability of these applications is determined, to some degree, by the reliability of the PECs that form the foundation of these applications, it is essential to explore the reliability improvement strategies adopted for these PECs. In fact, various works have been done in that regard. However, another question that arises from these reliability improvement strategies is "how much is enough" since improvement in one area might be at the cost of another factor. In an attempt to answer this question, this thesis addresses the growing demand for improved converter systems' reliability by proposing a reliability improvement optimization approach using Genetic Algorithm. The research investigates established reliability models for critical converter components and formulates various single and multi-objective optimization problems based on the models. Key parameters influencing failure rate and Mean Time To Failure were identified and integrated into the Genetic Algorithm framework. The framework combined Genetic Algorithm with the selected reliability prediction models, enabling a systematic exploration of the design space. Factors like component sizing, thermal management, switching frequency, and redundancy strategy are considered during the problem formulation.

Research Data and Supplementary Material