Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150531
Title: Multi-objective optimization for smaller, efficient and better performed design of buck-boost converters
Authors: Huang, Xianmiao
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Huang, X. (2021). Multi-objective optimization for smaller, efficient and better performed design of buck-boost converters. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150531
Abstract: Converters are essential components in DC-DC transformation and each objective of a converter plays an important role in the transformation. However, in most cases, improving one objective means sacrifices the others. As a result, the overall performance of the converters is not satisfied. The thesis proposes a method to optimized volume, efficiency, and cut-off frequency of LC-filter in buck-boost converter with full consideration of keeping three objectives on optimal conditions compared to the existed method. The Multi-objective optimization is for building a more portable, highly efficient, and better performance converter. For avoiding the interference of improving each objective and for obtaining optimal solutions with a fast process and better convergence, the author applies Non-dominated Sorting Genetic Algorithm-II to generate a Pareto frontier which could provide researchers a visualized figure to select the cases based on their demands. The multi-objective optimization results are compared with single-objective optimization results to verify the feasibility of the project.
URI: https://hdl.handle.net/10356/150531
DOI: 10.32657/10356/150531
Rights: This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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