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

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 characteri...

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Veröffentlicht in:IEEE transactions on industry applications 2021-09, Vol.57 (5), p.4924-4932
Hauptverfasser: Molsa, Eemeli, Tiitinen, Lauri, Saarakkala, Seppo E., Peretti, Luca, Hinkkanen, Marko
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Sprache:eng
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Zusammenfassung: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).
ISSN:0093-9994
1939-9367
1939-9367
DOI:10.1109/TIA.2021.3089458