A Review of RFO Induction Motor Parameter Estimation Techniques

Toliyat,; Lévi,; raina,

doi:10.1109/mper.2002.4312369

Cited by 105 publications

(143 citation statements)

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“…The obtained results confirm that the field-weakening operation can be performed in a very good way by the proposed BI-EKF-based estimator without knowledge of the IM's magnetizing curve obtained from additional attempts, as described in [3], which was mostly done in previous studies, such as in [13][14][15].…”

Section: The Field Weakening Operation (3≤ T ≤ 6 S )supporting

confidence: 75%

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Bi input-extended Kalman filter-based speed-sensorless control of an induction machine~capable of working in the field-weakening region

İnan¹,

Barut²

2014

Turk J Elec Eng & Comp Sci

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This study introduces a novel bi input-extended Kalman filter (BI-EKF)-based speed-sensorless direct vector control (DVC) of an induction motor (IM). The proposed BI-EKF-based estimator includes online estimations of the stator stationary axis components of the stator currents, isα and i sβ ; stator stationary axis components of the rotor flux, φrα and φ rβ ; rotor angular velocity, ωm ; stator resistance, Rs ; rotor resistance, Rr ; and load torque tL , as well as the magnetizing inductance, Lm , by only supposing that the stator phase currents and voltages are measured. Thus, the speed-sensorless DVC of the IM with the inclusion of the proposed estimator is able to be perfectly operated at a wide speed range, varying from zero speed to beyond the rated/based speed under the extreme variations in Rs , Rr , tL , and Lm . The simulations confirm the effectiveness of the proposed BI-EKF-based estimator and, consequently, the speed-sensorless DVC of the IM.

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Section: The Field Weakening Operation (3≤ T ≤ 6 S )supporting

confidence: 75%

“…However, the speed-sensorless control of IMs still constitutes a big challenge [1][2][3], since those estimations are negatively affected by:…”

Section: Introductionmentioning

confidence: 99%

Bi input-extended Kalman filter-based speed-sensorless control of an induction machine~capable of working in the field-weakening region

İnan¹,

Barut²

2014

Turk J Elec Eng & Comp Sci

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“…The error signal of the q-axis rotor flux controller is multiplied by the sign of the q-axis current in order to make The estimation models for the feedback quantities are formulated based on the stator steady-state voltage equations in the d-and q-axis and the flux linkage equations (6), (7) and (10)- (13). In the rotor flux reference frame, the d-and q-axis voltage equations derived from (8) at steadystate are…”

Section: Formulation Of Estimation Modelsmentioning

confidence: 99%

Magnetization Curve Identification of Vector-Controlled Induction Motor at Low-Load Conditions

Terzić

Despalatović

Slutej³

2012

Automatika

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Original scientific paperIn many drives the induction motor torque is transferred to the load through complex mechanical connections which produce an initial load. In such cases, to identify the magnetization curve by the no-load test it is necessary to decouple the motor from the transfer gear what is expensively and time-consuming. In an attempt to avoid the motor mechanical decoupling in the case of hoist crane systems, in this paper a method is proposed for the magnetization curve identification of the indirect rotor flux oriented (IRFO) induction machine at low-load conditions. Two flux controllers with estimated feedback quantities are added to the basic structure of the IRFO control drive. One of these controllers is used to control desired operating point on the magnetization curve and the other for on-line adaption of the reference frame position. The proposed algorithm is implemented in a commercial drive as a part of the self-commissioning module. Afterward, the magnetization curve of the 22 kW induction motor was identified and compared with the one obtained by the no-load test. The method sensitivity to the load torque and the transient inductance has also been considered. Key words: Induction motors, Magnetization curve, Parameter estimation, Vector controlOdredivanje krivulje magnetiziranja vektorski upravljanog asinkronog motora kod malih opterećenja. Brojni su primjeri pogona u kojima se moment asinkronog stroja prenosi na radni mehanizam preko mehaničkih reduktora koji mogu predstavljati značajno opterećenje motoru i u režimu neopterećenog pogona. Da bi se u takvim slučajevima odredila krivulja magnetiziranja iz pokusa praznog hoda nužno je odspojiti motor od reduktora što je skupo i vremenski zahtjevno. U pokušaju da se izbjegne mehaničko odspajanje motora kod dizaličnih sustava, u ovom je radu predložena metoda za odredivanje krivulje magnetizranja vektorski upravljanog asinkronog motora kod malih iznosa opterećenja. Dva regulatora ulančenog toka s estimiranim veličinama u granama povratne veze dodani su osnovnoj strukturi vektorski upravljanog pogona. Jedan od regulatora koristi se za upravljanje željenom radnom točkom na krivulji magnetiziranja dok se drugi koristi za podešavanje referentnog položaja vektora toka rotora. Predloženi algoritam implementiran je u komercijalnom uredaju kao jedan od samopodešavajućih modula koji se koriste prilikom puštanja u rad pogona. Nakon toga je opisanim postupkom odredena krivulja magnetiziranja 22 kW asinkronog motora i usporedena s krivuljom dobivenom iz klasičnog pokusa praznog hoda. Takoder je razmotrena i osjetljivost metode na iznos momenta opterećenja i tranzijentnog induktiviteta.

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“…Ongoing research has focused on providing sustained operation at high dynamic performance at very low speed, including zero speed and zero stator frequency (Akatsu & Kawamura, 2000;Holtz & Quan, 2002;Hurst et al, 1998;Lascu et al, 2005). In speed sensorless control, motor parameter sensitivity is an important and large discussed and analyzed problem (Akpolat et al, 2000;Toliyat et al, 2003). In many existing speed identification algorithms, the rotor speed is estimated based on the rotor flux observer.…”

Section: Introductionmentioning

confidence: 99%

Robust Control of Sensorless AC Drives Based on Adaptive Identification

Iulian¹

2011

Recent Advances in Robust Control - Theory and Applications in Robotics and Electromechanics

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A Review of RFO Induction Motor Parameter Estimation Techniques

Cited by 105 publications

References 0 publications

Bi input-extended Kalman filter-based speed-sensorless control of an induction machine~capable of working in the field-weakening region

Bi input-extended Kalman filter-based speed-sensorless control of an induction machine~capable of working in the field-weakening region

Magnetization Curve Identification of Vector-Controlled Induction Motor at Low-Load Conditions

Robust Control of Sensorless AC Drives Based on Adaptive Identification

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