A New Sensorless Control of Flux-Switching Permanent Magnet Machine Based on Magductance Saliency

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2024-12-24 DOI:10.1109/TIE.2024.3508137
Yang Jiang;Ming Cheng;Zhiyuan Xu
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Abstract

In most cases, the saliency ratio (Lq/Ld) of flux-switching permanent magnet (FSPM) machines is low because the field modulation effect brings small equivalent reluctance saliency. Thus, it hinders the sensorless control dependent on reluctance saliency. To solve this problem, a novel magductance saliency, the difference between magductance of d-axis and that of q-axis, is introduced through adding short-circuited coils in the stator of FSPM machine. Consequently, the saliency ratio of FSPM machines is increased. Additionally, the issue of multiple saliencies presents a significant drawback, negatively impacting the performance of sensorless control, especially when the machine has a low-saliency ratio. Thus, the position observer based on second-order generalized integrator frequency locked loop (SOGI-FLL) is applied to solve this problem. Finally, the proposed method is validated on a 6-stator-pole/19-rotor-tooth FSPM machine, demonstrating its effectiveness.
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基于磁导显著性的新型无传感器磁通开关永磁电机控制
在大多数情况下,磁通开关永磁(FSPM)电机的显着比(Lq/Ld)较低,因为磁场调制效应带来了较小的等效磁阻显着。因此,它阻碍了依赖于磁阻显着性的无传感器控制。为了解决这一问题,通过在FSPM电机定子中加入短路线圈,引入了一种新的磁导显著性,即d轴与q轴的磁导差。因此,提高了FSPM机的显着率。此外,多显着性的问题提出了一个显著的缺点,对无传感器控制的性能产生负面影响,特别是当机器具有低显着性比率时。因此,采用基于二阶广义积分器锁频环(SOGI-FLL)的位置观测器来解决该问题。最后,在6定子-极/19转子-齿FSPM机床上进行了验证,验证了该方法的有效性。
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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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