Cycle Estimation-Based Deadbeat Interleaving Method for Critical Mode Totem-Pole Rectifiers

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2024-11-19 DOI:10.1109/TIE.2024.3493174
Mingde Zhou;Qishan Pan;Minfan Fu;Junrui Liang;Haoyu Wang
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Abstract

In totem-pole boost rectifiers, critical mode (CRM) operation is associated with varying switching frequency and duty ratio. This makes it difficult to precisely manage the phase shift between interleaved phases. To address this challenge, we propose a novel interleaving method based on cycle estimation. The switching cycle is estimated by predicting the zero-crossing instants. By analyzing the input current ripple, the relationship among phase shift, duty ratio, and switching cycle can be derived. Therefore, after frequency transition, the phase shift and switching cycle can be updated to provide a precise deadbeat control. Different from conventional methods, the proposed interleaving methods can dynamically modulate the phase shift without the usage of high-speed current sensors. A 1.6 kW, 160–950 kHz GaN-based, two-phase interleaved totem-pole rectifier is designed as the proof of concept. 98.24% peak efficiency and 0.995 power factor are captured. Experimental results effectively validate the interleaving method.
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基于周期估计的临界模式图腾极整流器死区交错法
在图腾极升压整流器中,临界模式(CRM)操作与不同的开关频率和占空比有关。这使得很难精确地管理交错相位之间的相移。为了解决这一挑战,我们提出了一种基于周期估计的交错方法。通过预测过零瞬间来估计开关周期。通过分析输入电流纹波,推导出相移、占空比和开关周期之间的关系。因此,在频率转换后,相移和开关周期可以更新,以提供精确的无差拍控制。与传统方法不同的是,该方法可以在不使用高速电流传感器的情况下动态调制相移。设计了一个1.6 kW, 160-950 kHz基于氮化镓的两相交错图腾极整流器作为概念验证。峰值效率为98.24%,功率因数为0.995。实验结果有效地验证了交错法的有效性。
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来源期刊
IEEE Transactions on Industrial Electronics
IEEE Transactions on Industrial Electronics 工程技术-工程:电子与电气
CiteScore
16.80
自引率
9.10%
发文量
1396
审稿时长
6.3 months
期刊介绍: Journal Name: IEEE Transactions on Industrial Electronics Publication Frequency: Monthly Scope: The scope of IEEE Transactions on Industrial Electronics encompasses the following areas: Applications of electronics, controls, and communications in industrial and manufacturing systems and processes. Power electronics and drive control techniques. System control and signal processing. Fault detection and diagnosis. Power systems. Instrumentation, measurement, and testing. Modeling and simulation. Motion control. Robotics. Sensors and actuators. Implementation of neural networks, fuzzy logic, and artificial intelligence in industrial systems. Factory automation. Communication and computer networks.
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