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Standstill Identification of an Induction Motor Model Including Deep-Bar and Saturation Characteristics

机译:静止识别感应电动机模型,包括深杆和饱和特性

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This article deals with standstill identification of an induction motor drive for sensorless self-commissioning purposes. The proposed identification method is based on an advanced model of a squirrel-cage induction motor. The model includes the deep-bar effect and the magnetic saturation characteristics. The excitation signals are fed to the stator using a standard inverter without compensating for its nonlinearities. The saturable stator inductance is first identified by means of a robust flux-integration test, where unknown voltage disturbances are canceled with suitably selected current pulses. Then, the deep-bar characteristics are identified by means of a dc-biased sinusoidal excitation using different frequencies. Finally, the cross-saturation characteristics of the rotor leakage inductance are identified by altering the dc bias of the excitation signal. The identified characteristics are transformed to the parameters of the advanced motor model taking into account the interrelations of the aforementioned phenomena. Since the physical phenomena affecting the standstill identification process are properly included in the identified model, fewer approximations are needed and more accurate parameter estimates are obtained. The identification procedure is validated by means of experiments using two different induction motors (5.6 and 45 kW).
机译:本文涉及无传感器自调试目的的感应电机驱动的静止识别。所提出的识别方法基于鼠笼式感应电动机的先进模型。该模型包括深杆效应和磁饱和特性。使用标准逆变器将激励信号馈送到定子,而不会补偿其非线性。 The saturable stator inductance is first identified by means of a robust flux-integration test, where unknown voltage disturbances are canceled with suitably selected current pulses.然后,通过使用不同频率的DC偏置正弦激发来识别深杆特性。最后,通过改变激励信号的直流偏压来识别转子漏电电感的交叉饱和特性。考虑到上述现象的相互关系,将所识别的特性转化为先进电动机模型的参数。由于影响静止识别过程的物理现象被正确地包括在所识别的模型中,因此需要较少的近似,并且获得更准确的参数估计。通过使用两种不同的感应电动机(5.6和45kW)的实验验证识别程序。

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