AI-based design of buck converter

Multi-objective optimization of buck converter was done in CCM working condition in many researches. However, few researches focused on the optimization in DCM. In this report, three-objective optimization designed based on the buck converter which worked in discontinuous conduction mode. The equati...

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Main Author: Huang, Xianmiao
Other Authors: Jack Zhang Xin
Format: Final Year Project
Language:English
Published: 2019
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Online Access:http://hdl.handle.net/10356/77346
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-773462023-07-07T16:30:59Z AI-based design of buck converter Huang, Xianmiao Jack Zhang Xin School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Power electronics Multi-objective optimization of buck converter was done in CCM working condition in many researches. However, few researches focused on the optimization in DCM. In this report, three-objective optimization designed based on the buck converter which worked in discontinuous conduction mode. The equations of PC, Pon, PCu, PFe were different from the equations in CCM or BCM. In the first steps of this project, I deduced those equations and prepared all the parameters which was useful in the part B, which was a part to generate Pareto front based on 3 dimensions, including power loss, cutoff frequency and volume. NSGA-II, a kind of AI technology, was applied to generate Pareto frontier in this project. NSGA-II is an elitist and fast GA (genetic algorithm) evolved from basic genetic algorithm. In this report, the process of using NSGA-II to generate Pareto frontier was showed in details, including how the crossover process worked and the analysis of crowding distance sorting. I chose three extreme cases as three models from all the points in Pareto front. Case 1 had the minimum of power loss, while cutoff frequency is minimum among all the solutions in case 2. And the volume of filter was the minimal figure in the last case. In the final section in my project, I did a simulated experiment by MATLAB to visualize the difference between the working results of three extreme cases. And this will provide help designers of buck converter to select the suitable pairs of L and C. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-05-27T07:33:56Z 2019-05-27T07:33:56Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77346 en Nanyang Technological University 54 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Power electronics
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Power electronics
Huang, Xianmiao
AI-based design of buck converter
description Multi-objective optimization of buck converter was done in CCM working condition in many researches. However, few researches focused on the optimization in DCM. In this report, three-objective optimization designed based on the buck converter which worked in discontinuous conduction mode. The equations of PC, Pon, PCu, PFe were different from the equations in CCM or BCM. In the first steps of this project, I deduced those equations and prepared all the parameters which was useful in the part B, which was a part to generate Pareto front based on 3 dimensions, including power loss, cutoff frequency and volume. NSGA-II, a kind of AI technology, was applied to generate Pareto frontier in this project. NSGA-II is an elitist and fast GA (genetic algorithm) evolved from basic genetic algorithm. In this report, the process of using NSGA-II to generate Pareto frontier was showed in details, including how the crossover process worked and the analysis of crowding distance sorting. I chose three extreme cases as three models from all the points in Pareto front. Case 1 had the minimum of power loss, while cutoff frequency is minimum among all the solutions in case 2. And the volume of filter was the minimal figure in the last case. In the final section in my project, I did a simulated experiment by MATLAB to visualize the difference between the working results of three extreme cases. And this will provide help designers of buck converter to select the suitable pairs of L and C.
author2 Jack Zhang Xin
author_facet Jack Zhang Xin
Huang, Xianmiao
format Final Year Project
author Huang, Xianmiao
author_sort Huang, Xianmiao
title AI-based design of buck converter
title_short AI-based design of buck converter
title_full AI-based design of buck converter
title_fullStr AI-based design of buck converter
title_full_unstemmed AI-based design of buck converter
title_sort ai-based design of buck converter
publishDate 2019
url http://hdl.handle.net/10356/77346
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