A Model Predictive Control of Three-Level Cascaded Noninverting Buck–Boost Converter for Energy Storage System

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2024-11-05 DOI:10.1109/TIE.2024.3481950
Qiaoling Peng;Simiao Zhou;Fujun Ma;Gelin Huang;Rui Fan
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Abstract

The three-level cascaded noninverting buck–boost converter (TL-CNIBBC) has various operating modes and is especially suited at wide-range voltage bidirectional conversion for energy storage. How to choose a suitable operating mode and realize smooth mode switching is the key problem to be solved. In this article, the operation principle of TL-CNIBBC is analyzed, and two extended modes based on the traditional working modes are introduced to eliminate the dead zone when the input voltage is close to the output voltage. Second, a model predictive control (MPC) with inductor current as a cost function is proposed, taking the hysteresis loop control to avoid mode jumping with mode switching rules defined to achieve multimode smooth transition. Finally, to address the capacitor voltage imbalance in three-level converter, a modification of the duty ratio is proposed. Experimental results show the proposed control strategy can select the proper working modes effectively and realize smooth mode switching.
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用于储能系统的三电平级联非反相降压-升压转换器的模型预测控制
三电平级联非反相降压升压变换器(TL-CNIBBC)具有多种工作模式,特别适用于大范围电压双向转换的储能。如何选择合适的工作模式,实现模式的平稳切换是需要解决的关键问题。本文分析了TL-CNIBBC的工作原理,在传统工作模式的基础上引入了两种扩展模式,消除了输入电压接近输出电压时的死区。其次,提出了一种以电感电流为代价函数的模型预测控制(MPC),通过定义模式切换规则,采用滞回环控制避免模式跳变,实现多模平滑过渡。最后,针对三电平变换器中电容电压不平衡的问题,提出了一种修改占空比的方法。实验结果表明,所提出的控制策略能够有效地选择合适的工作模式,并实现平稳的模式切换。
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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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