Stable MRAS-based sensorless scheme design strategy for high power traction drives

Vicente, Iban; Brown, M.; Renfrew, A.C.; Endemaño, Aitor; Garin, X.

doi:10.1049/cp:20080584

Cited by 3 publications

(1 citation statement)

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“…So far, the sensorless drives have been successfully applied in medium-and high-speed applications, but their low-and zero-speed operations under steady and transient state are still a critical issue [16,17,31]. The parameter variations, inverter non-linearity and signal distortions are the primary causes of such unsatisfactory operation of IM drives [32,33].…”

Section: Iet Electric Power Applicationsmentioning

confidence: 99%

Review on model reference adaptive system for sensorless vector control of induction motor drives

Kumar

Das

Syam

et al. 2015

IET Electric Power Applications

189

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The most frequently used electrical machine in various modern high-performance drive applications is the induction motor (IM), especially its squirrel cage type rotor counterpart. The high-precision, efficient control of sensorless IM drives throughout its operating range demands an exact knowledge of a few of the IM parameters. The stator resistance, rotor resistances are such important IM parameters whose variations may lead to improper estimation of speed over the whole operating range in sensorless drives. Numerous methods for the estimation of speed of high-performance sensorless IM drives taking into account the effect of parameter variations have been developed. This paper aims at providing a review of the major model reference adaptive system (MRAS) based techniques applied for the estimation of speed of such sensorless drives. This paper is illustrated throughout with relevant mathematical equations, experimental and simulation results, associated to different MRAS-based sensorless vector control of the IM drive.Nomenclature v s stator voltage vector i s stator current vector i m = i s + (L r /L m )i r ,î m magnetising and estimated current vector e m back EMF vector (V) v ds , v qs d-axis and q-axis components of stator voltage (V) i ds , i qs d-axis and q-axis components of stator current (A) c dr , c qr d-axis and q-axis components of rotor flux (Wb) c dr ,ĉ qr estimated d-axis and q-axis components of rotor flux (Wb) c r ,ĉ r rotor flux (Wb), estimated rotor flux (Wb) K p , K i proportional, integral gain constants ε r , ε rotor-flux-error signal (Wb), error signal R r R s rotor, stator resistance referred to stator (Ω) L r , L s , L m rotor self-, stator self-, mutual inductance referred to stator (H) X r , X s , X m rotor self-, stator self-, mutual reactance referred to stator (Ω) L ′ m = L 2 m /L r equivalent mutual inductance (H) T r = L r /R r rotor time constant (s) s = 1 − L 2 m /(L s L r ) total machine leakage factor v r , ω r , v * r estimated, actual and reference rotor speed (rad/s) Δe s error in estimated back EMF (V) p derivative operator k ωr speed gain q m ,q m instantaneous and estimated reactive power volt-ampere reactive (VAR) ω e , Δω e speed and speed-deviation of rotating reference frame (rad/s) e ds, qs = i ds, qs −î ds, qs error between measured and estimated stator currents (A) e c control signal based on combined stator current error and rotor flux

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