Qingle Sun;Zifan Lin;Zhifu Wang;Herbert Ho Ching Iu;Tyrone Fernando;Xinan Zhang
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引用次数: 0
摘要
为了提高有限集模型预测控制(FS-MPC)的系统性能,研究了离散空间矢量调制(DSVM)和加权因子消去技术。然而,现有的基于dsvm的无权重因子的三电平逆变器FS-MPC计算量大,算法灵活性低,工作范围大大缩小。为了解决这些问题,本文提出了一种基于虚拟中压矢量(virtual medium voltage vectors, MVVs)的新型DSVM- FS-MPC,该方法大大降低了电流谐波和计算负担,实现了DSVM中灵活可调的时间间隔数量,并消除了加权因子,同时保证了中性点电压(NPV)在整个工作频率和负载条件下的平衡。引入的虚拟mvv允许候选集始终缩小到最接近参考向量的电压向量。提出了一种简单的判断方案来确定是否应该使用真实的或虚拟的mpv,从而可以灵活地约束NPV的最大误差。还实现了基于运营商的实现。实验结果验证了该算法的有效性。
Finite-Set Model Predictive Control With Virtual Medium Voltage Vectors and Eliminated Weighting Factors for 3L-NPC Inverters
Discrete space vector modulation (DSVM) and weighting factor elimination have been investigated to improve the system performance of finite-set model predictive control (FS-MPC). However, the existing DSVM-based FS-MPC without weighting factors for three-level inverters suffers from high computational burden, low algorithm flexibility, and substantially narrowed operating range. To address these issues, this article proposes a novel DSVM-based FS-MPC with the virtual medium voltage vectors (MVVs), which greatly reduces the current harmonics and computational burden, realizes a flexibly adjustable number of time intervals in DSVM, and eliminates the weighting factor while ensuring balanced neutral-point voltage (NPV) over the full range of operating frequencies and load conditions. The introduced virtual MVVs allow the candidate set to be always narrowed down to the voltage vector closest to the reference vector. A simple judgment scheme is proposed to determine whether a real or virtual MVV should be used, which enables a flexible constraint on the maximum NPV error. Carrier-based implementation is also achieved. Experimental results validate the proposed algorithm.
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.