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

Zaky, Mohamed S.; Khater, Mostafa M. A.; Yasin, H.; Shokralla, S. S.

doi:10.1016/j.epsr.2009.07.012

Cited by 37 publications

(31 citation statements)

References 13 publications

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“…The model-based techniques are designed to obtain the speed and other variable information using the measurement of voltage and current at IM terminals. Various approaches have been proposed, such as model reference adaptive system (MRAS) [1], sliding-mode observers [2][3], artificial intelligence techniques [4], and Kalman-filter observers [5]. It is well known that the modelbased estimation schemes have a constraint when the stator frequency approaches zero since the rotor-induced voltage becomes zero, and accordingly the control system turn out to be unobservable.…”

Section: Introductionmentioning

confidence: 99%

Using NSGA II multiobjective genetic algorithm for EKF-based estimation of speed and electrical torque in AC induction machines

Alsofyani

Idris

Jannati

et al. 2014

2014 IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014)

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High-performance AC drives require accurate speed and torque estimations to provide a proper system operation. The selection and quality of extended Kalman fitter (EKF) covariance elements have a considerable bearing on the effectiveness of motor drive. Many EKF-based optimization techniques involve only a single objective for the optimal estimation of speed without giving concern to the torque. This paper presents a new methodology for the selection of EKF filters that uses non-dominated sorting genetic algorithm-II (NSGA-II) developed for filter element selection in order to investigate the concurrent optimization of speed and torque. The proposed optimizing technique for EKF-based estimation scheme is used in the combination with the sensorless direct torque control of induction motor. The multi-optimal based-EKF is tested with three regions of Pareto front curve.

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Section: Introductionmentioning

confidence: 99%

Using NSGA II multiobjective genetic algorithm for EKF-based estimation of speed and electrical torque in AC induction machines

Alsofyani

Idris

Jannati

et al. 2014

2014 IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014)

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“…For the solution of the problems associated with the speed-sensorless control of IMs, a variety of estimation methods [4][5][6][7][8], which use deterministic IM models, have been very recently introduced in the literature; however, * Correspondence: mbarut@nigde.edu.tr they are especially sensitive to variations in R s and/or R r , together with variations in L m . In addition to those, studies like those in [9][10][11][12] undertake performing both R s and R r estimations without L m estimation.…”

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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“…However, some limitations on operation of conventional observers were large chattering on the speed, which very low speeds quickly highlighted. [8][9].…”

Section: Introductionmentioning

confidence: 99%

“…These observers are used to observe the flux and speed estimation in a closed loop [4] [6][7]. Although such approaches lead to different performance in relation to the degree of algorithmic and computational complexity, they generally offer good performance in a wide range of speed but cannot estimate very low speeds in a stable manner [8][9].…”

Section: Introductionmentioning

confidence: 99%

Reduced-Order Sliding Mode Observer-based Speed Sensorless Vector Control of Double Stator Induction Motor

2014

APH

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In this paper a reduced-order sliding mode observer-based speed sensorless vector control of a double stator induction motor (DSIM) is presented. The conventional reduced-order observer has large chattering on the speed and the torque. In order to solve this problem, the sliding mode technique with smooth function is used. The simulation testsshow the effectiveness of the proposed method especially in the load disturbances and/or the change of the reference speed.

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

Cited by 37 publications

References 13 publications

Using NSGA II multiobjective genetic algorithm for EKF-based estimation of speed and electrical torque in AC induction machines

Using NSGA II multiobjective genetic algorithm for EKF-based estimation of speed and electrical torque in AC induction machines

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

Reduced-Order Sliding Mode Observer-based Speed Sensorless Vector Control of Double Stator Induction Motor

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