Adaptive Dead-Time and Partial-ZVS Regulation for GaN-Based Active Clamp Flyback Converter With Predictive Hysteresis Current Mode Control

控制理论(社会学) 电感 漏感 死时间 变压器 电容器 计算机科学 电磁线圈 热传导 电子工程 电压 工程类 材料科学 电气工程 数学 控制(管理) 统计 人工智能 复合材料
作者
Yingyi Yan,Tingying Wang,Yazhou Wang,Meiling Zhu,Hairui Tang,Qinsong Qian
出处
期刊:IEEE Transactions on Power Electronics [Institute of Electrical and Electronics Engineers]
卷期号:38 (9): 10782-10797
标识
DOI:10.1109/tpel.2023.3286839
摘要

Active clamp flyback (ACF) converter is regarded as a good candidate in high-frequency small-size adapters. With adaptive dead-time optimization method, there is no reverse conduction loss and switching- on loss in power switches, then ACF can realize optimum full-ZVS control at every operating conditions. However, the other losses such as the conduction loss of clamp switch, the primary winding loss of transformer may be increased to counteract this efficiency advantage, but which has not been outlined. Therefore, this paper proposes a higher-efficiency predicted hysteresis current mode control strategy with adaptive dead-time and partial-ZVS regulation for GaN-based ACF converter for the first time. First, the accurate analytical model by considering above losses is derived. Second, the optimal valley current is calculated by the minimum loss, to determine the conduction time of clamp switch and the dead time of main switch. Finally, a digital controlled GaN-based ACF converter is designed to verify the method. The computation speed of needed analytical equations and the magnetizing inductance offset are considered in implementation process. The experimental results show the proposed method can improve efficiency by 0.4% than existing method, which also show a fast dynamic response since the cycle-by-cycle variable-frequency control concept.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
研友_LOqqmZ完成签到,获得积分10
2秒前
2秒前
英俊的铭应助文献查找采纳,获得10
2秒前
solobang发布了新的文献求助10
2秒前
Jasper应助老迟到的书雁采纳,获得10
5秒前
orixero应助小二采纳,获得10
5秒前
6秒前
6秒前
simple完成签到,获得积分10
6秒前
caoyy发布了新的文献求助10
6秒前
赵小可可可可完成签到,获得积分10
8秒前
小萌发布了新的文献求助10
9秒前
weiv发布了新的文献求助10
9秒前
海科科发布了新的文献求助10
10秒前
陌上花完成签到,获得积分10
10秒前
我是站长才怪应助微笑襄采纳,获得10
11秒前
caoyy完成签到,获得积分10
12秒前
JamesPei应助独特亦旋采纳,获得10
13秒前
14秒前
14秒前
科目三应助Jenny采纳,获得50
16秒前
gry发布了新的文献求助10
17秒前
Hh发布了新的文献求助10
19秒前
Jzhang应助daniel采纳,获得10
19秒前
19秒前
夏夏发布了新的文献求助10
19秒前
jiesenya完成签到,获得积分10
21秒前
今后应助smile采纳,获得10
21秒前
万能图书馆应助wuzhizhiya采纳,获得10
22秒前
科研通AI5应助清新的静枫采纳,获得10
22秒前
applelpypies完成签到 ,获得积分10
22秒前
内向一笑完成签到 ,获得积分10
23秒前
ll完成签到,获得积分20
23秒前
23秒前
444完成签到,获得积分10
24秒前
gry完成签到,获得积分10
26秒前
26秒前
科研通AI5应助夏夏采纳,获得10
27秒前
LU完成签到 ,获得积分10
27秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
Luis Lacasa - Sobre esto y aquello 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527990
求助须知:如何正确求助?哪些是违规求助? 3108173
关于积分的说明 9287913
捐赠科研通 2805882
什么是DOI,文献DOI怎么找? 1540119
邀请新用户注册赠送积分活动 716941
科研通“疑难数据库(出版商)”最低求助积分说明 709824