Estimating current derivatives for sensorless motor drive applications

Estimating current derivatives for sensorless motor drive applications
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估算无传感器电机驱动应用的电流导数

DOI:
10.1109/epe.2015.7311672
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发表时间:
2015
期刊:
--
影响因子:
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通讯作者:
Hind D
Hind D
中科院分区:
--
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作者:
Hind D

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用于交流电机无传感器控制的PWM电流导数技术需要在特定PWM矢量下测量电流导数。由于高频(HF)振荡影响电流和电流导数响应,这在窄PWM矢量下通常是不可能的。在以前的工作中,研究人员将PWM矢量应用于机器的时间延长到称为最小脉冲宽度(tmin)的阈值,以便允许HF振荡衰减并获得导数测量。新的实验结果表明,人工神经网络(ANN)可以用来估计衍生物使用标准电流传感器的测量之前,高频振荡已经完全衰减。这降低了所需的最小脉冲宽度,并可显著降低额外的电流失真和转矩涟漪。
The PWM current derivative technique for sensorless control of AC machines requires current derivative measurements under certain PWM vectors. This is often not possible under narrow PWM vectors due to high frequency (HF) oscillations which affect the current and current derivative responses. In previous work, researchers extended the time that PWM vectors were applied to the machine for to a threshold known as the minimum pulse width (tmin), in order to allow the HF oscillations to decay and a derivative measurement to be obtained. This resulted in additional distortion to the motor current New experimental results demonstrate that an artificial neural network (ANN) can be used to estimate derivatives using measurements from a standard current sensor before the HF oscillations have fully decayed. This reduces the minimum pulse width required and can significantly reduce the additional current distortion and torque ripple.
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