Term of Award
Master of Science, Electrical Engineering
Document Type and Release Option
Thesis (restricted to Georgia Southern)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Electrical and Computer Engineering
Committee Member 1
Committee Member 2
Utilizing power electronic converters in power systems as direct energy conversion stages brings designers and on-site operators reliability challenges. Reliability evaluation of power converters is essential for optimal design, control methodology selection, optimal operation, availability, and maintenance schedule. Therefore, having a practical and robust framework to determine the failure-prone components and the cause of their failures, converters' end of life, and reliability evaluation can avoid interrupting or deducting energy and costly maintenance of power systems. On the other hand, traditional reliability estimation approaches have imposed challenges. This procedure is very time-consuming and parameter dependent. Therefore, this work studies converter reliability concepts, factors affecting the failure of elements, and the conventional approaches. Also, the presented nonparametric method makes designers and specialists observe the converter with specified parameters before further developing control strategies and maintenance. The model considers mission profiles as the main stressors and predicts converters' failure (or lifetime).
Alvand, Hamideh, "Reliability Analysis of Power-Converters-Based Power Systems using Artificial Intelligence" (2022). Electronic Theses and Dissertations. 2483.
Research Data and Supplementary Material