Speed-Adaptive Observer Design for Sensorless Induction Motor Drives in Ultra-Low Speed Region With Graphical Method

IF 7.2 1区 工程技术 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Industrial Electronics Pub Date : 2024-12-25 DOI:10.1109/TIE.2024.3503608
Bo Wang;Xiancheng Huang;Pengcheng Du;Dianguo Xu
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Abstract

In the traditional speed adaptive full-order observers of induction motor (IM), since the actual value of rotor flux is unattainable, the flux error term is usually ignored in the speed adaptive law. This would cause instability at low-speed during regenerating load. To mitigate this issue, an improved speed adaptive law strategy is proposed by introducing the d-axis current error term. Compared with the conventional speed adaptive law, the studied method maintains stability across the wide speed range based on the Routh–Hurwitz stability criterion. Notably, the studied method necessitates only a single parameter design, enhancing its practical applicability. Furthermore, the formula of the improved speed adaptive law is solely dependent on the motor inductance parameter. Consequently, even with variations in inductance, it can still retain its stable operation area. Experimental results from a 2.2 kW IM platform validate the effectiveness of the studied method.
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超低速区无传感器感应电机速度自适应观测器的图形化设计
在传统的异步电动机速度自适应全阶观测器中,由于转子磁链的实际值难以获得,因此在速度自适应律中往往忽略磁链误差项。这将导致在低速再生负荷期间不稳定。为了解决这一问题,提出了一种改进的速度自适应律策略,引入了d轴电流误差项。与传统的速度自适应律相比,该方法基于Routh-Hurwitz稳定性判据在较宽的速度范围内保持稳定性。值得注意的是,所研究的方法只需要单个参数设计,增强了其实用性。此外,改进后的速度自适应律公式完全依赖于电机电感参数。因此,即使电感变化,它仍然可以保持其稳定的工作区域。在一个2.2 kW的IM平台上的实验结果验证了所研究方法的有效性。
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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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