Low-speed sensorless control of induction Machine

Edelbaher, G.; Jezernik, K.; Urlep, E.

doi:10.1109/tie.2005.862307

Cited by 60 publications

(32 citation statements)

References 10 publications

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“…Alternatively, speed information can be obtained by using the machine model and its terminal variables like voltage and current. These include different methods such as model reference adaptive systems (MRAS) [10]; extended Kalman filters (EKF) [12]; adaptive flux observer [9]; artificial intelligence techniques [13]; and sliding mode observer (SMO) [7].…”

Section: Introductionmentioning

confidence: 99%

“…These schemes rely on stator current measurement and mostly require information regarding stator voltages as well [6][7][8][9][10][11][12][13]. The first category of them includes different types of estimators which often use an adaptive mechanism to update the value of stator resistance [7][8][9][10][11]. The stator resistance is determined in ref.…”

Section: Introductionmentioning

confidence: 99%

“…The stator resistance is determined in ref. [7] by using a reactive power based model reference adaptive system. The reactive power relies on the accuracy of other parameters, such as leakage inductance and rotor resistance, which are not necessarily constant and the result is prone to error.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Very low speed and zero speed estimations of sensorless induction motor drives

Zaky

Khater

Yasin

et al. 2010

Electric Power Systems Research

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Very low speed and zero speed estimations of sensorless induction motor drives

Zaky

Khater

Yasin

et al. 2010

Electric Power Systems Research

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“…Furthermore, strategies based on IM spatial saliency methods with fundamental excitation and high frequency signal injection [12], extended Kalman filter techniques and adaptive system approaches [18] have been studied. The sensorless control of IM allowing operation at very low speed can also be found in [7], [8], [9], [10], [13], [21].…”

Section: Introductionmentioning

confidence: 99%

On Sensorless Induction Motor Drives: Sliding-Mode Observer and Output Feedback Controller

Ghanes

Zheng

2009

IEEE Trans. Ind. Electron.

107

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Abstract-In this paper, a sensorless output feedback controller is designed in order to drive the Induction Motor (IM) without the use of flux and speed sensors. Firstly, a new sliding mode observer that uses only the measured stator currents is synthesized to estimate the speed, the flux and the load torque. Secondly, a current-based field oriented sliding mode control is developed so as to steer the estimated speed and flux magnitude to the desired references. Stability analysis based on Lyapunov theory is also presented in order to guarantee the closed loop stability of the proposed observer-control system. Two experimental results for a 1.5-kW IM are presented and analyzed by taking into account the unobservability phenomena of the Sensorless Induction Motor (SIM).

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“…Recently, for the solution of the problem zero/very low speed, model-based estimation methods have been proposed, such as in [6]- [8], specifically addressing persistent operation zero speed. Among those studies [6] uses a total-least-squarebased speed adaptive flux observer which enables zero-statorfrequency operation over an interval of 60 s, with mean and maximum estimation error values of 1.34 and 38 r/min, respectively, at zero load.…”

Section: Introductionmentioning

confidence: 99%

Speed-Sensorless Estimation for Induction Motors Using Extended Kalman Filters

Barut

Boğosyan

Gökaşan

2007

IEEE Trans. Ind. Electron.

298

106

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In this paper, extended-Kalman-filter-based estimation algorithms that could be used in combination with the speed-sensorless field-oriented control and direct-torque control of induction motors (IMs) are developed and implemented experimentally. The algorithms are designed aiming minimum estimation error in both transient and steady state over a wide velocity range, including very low and persistent zero-speed operation. A major challenge at very low and zero speed is the lost coupling effect from the rotor to the stator, which makes the information on rotor variables unobservable on the stator side. As a solution to this problem, in this paper, the load torque and the rotor angular velocity are simultaneously estimated, with the velocity taken into consideration via the equation of motion and not as a constant parameter, which is commonly the case in most past studies. The estimation of load torque, on the other hand, is performed as a constant parameter to account for Coulomb and viscous friction at steady state to improve the estimation performance at very low and zero speed. The estimation algorithms developed based on the rotor and stator fluxes are experimentally tested under challenging variations and reversals of the velocity and load torque (step-type and varying linearly with velocity) over a wide velocity range and at zero speed. In all the scenarios, the current estimation error has remained within a very narrow error band, also yielding acceptable velocity estimation errors, which motivate the use of the developed estimation method in sensorless control of IMs over a wide velocity range and persistent zero-speed operation.Index Terms-Extended Kalman filter (EKF), induction motor (IM), low/zero-speed operation, sensorless control.

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Low-speed sensorless control of induction Machine

Cited by 60 publications

References 10 publications

Very low speed and zero speed estimations of sensorless induction motor drives

Very low speed and zero speed estimations of sensorless induction motor drives

On Sensorless Induction Motor Drives: Sliding-Mode Observer and Output Feedback Controller

Speed-Sensorless Estimation for Induction Motors Using Extended Kalman Filters

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