A new speed estimation scheme of the induction motor considering the flux saturation

Choi, Jeong‐Hoon; Han, Yoon-Seok; Kim, Young-Seok

doi:10.1109/20.908912

Cited by 3 publications

(1 citation statement)

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“…The estimation algorithms in [11] and [12] are only executable whenever the speed-sensorless control system reaches its steady-state, which was declared by the authors. Attempts for including L m -estimation into speed-sensorless estimation algorithms were also made in [13][14][15][16]; however, the algorithms in [13,15,16] were sensitive to variations in both R s and R r , while the study in [14] was affected by R r variations.…”

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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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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