Sensorless indirect vector control of an induction motor by ANNs observer and EKF

This paper presents a rotor position estimator for doubly fed induction motor (DFIM) based on carrier signal injection. The proposed control uses a high frequency signal injection methods to estimate the speed of doubly fed induction motor, which is used as feedback of vector control algorithm. This method is based on superimposed with the voltage stator a high frequency signal. The resulting voltage is applied in the stator windings using a pulse with modulated voltage-source inverter. The injection of these signal induced a rotor high frequency current, which contains the rotor position information, from which the rotor speed is estimated. In order to implement this estimator, a sensorless control structure based on stator field oriented control is investigated in which two voltage inverters are used to supply the stator and rotor sides. The simulation results implemented on Matlab/Simulink shows effectiveness under various operating conditions (Low speed, nominal speed and high speed).

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“…However these sensors have several disadvantages in terms of drive cost, reliability, and noise immunity [1], [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Rotor speed estimation of doubly fed induction motor using high frequency carrier signal injection

Chibah

Menaa

Yazid

2014

2014 16th International Power Electronics and Motion Control Conference and Exposition

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“…From the view of drive system, elimination of the speed sensors and the associated measurement is convenient to use an estimator, which offers both speed and flux estimation. Several kinds of estimators meet these requirements, such as model reference adaptive systems (MRAS) [1], artificial neural networks (ANN) [2], high-frequency signal injection [3], full-order adaptive observer [4], sliding-mode observer (SMO) [5], and extended kalman filter (EKF) [6]- [9], etc.…”

Section: Introductionmentioning

confidence: 99%

A speed and flux estimation method of induction motor using fuzzy extended kalman filter

Xiao

Sun

Liu

et al. 2014

2014 International Power Electronics and Application Conference and Exposition

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A speed and flux estimation method of induction motors using fuzzy extended kalman filter(FEKF) is proposed in this paper, which is used to make lower impact of time varied statistic of measurement noise. It reaches a better speed estimation accuracy of induction motors than the extended kalman filter(EKF). The proposed algorithm modifies the measurement noise covariance of extended kalman filter recursively by monitoring if the ratio between filter's innovation and actual innovation is near 1, and chooses a fuzzy factor to make its noise model close to real noise model adaptively. The speed estimated error and the flux fluctuation of FEKF under gross external disturbance and unknown measurement noises are compared with EKF. Simulation and experimental results show that FEKF provides better performance and faster convergence than EKF under gross external error and unknown measurement noises.

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“…By using estimated flux information the speed of the induction motor was calculated. The full order observers in [3] - [10] estimate all the state variables and are sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Rotor Flux using Neural Network Observer in Speed Sensorless Induction Motor Drive

KamalBasha¹,

Suryakalavathi²

2013

IJCA

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In speed sensorless controlled induction motor drive, the rotor flux based Model Reference Adaptive System (MRAS) for is more popular. In MRAS, the voltage model equations are used in the reference model, which leads to poor performance of the drive at low speeds. In this paper, a new stator current based MRAS is presented. In this measured stator currents of induction motor are used as a reference model to avoid the use of a pure integrator. Estimated stator currents are used as an adjustable model. The estimation of stator currents requires the information of the rotor flux which can be obtained from the voltage or current model. This encounters instability and dc drift. To overcome these problems, flux observer is proposed based on Neural Network (NN). This offline trained multilayer feed-forward NN flux observer applied to indirect vector controlled speed sensorless induction motor drive has been verified by MATLAB/SIMULINK. General TermsNeural Networks, Power Electronics and Drives et. al.

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Sensorless indirect vector control of an induction motor by ANNs observer and EKF

Cited by 14 publications

References 8 publications

Rotor speed estimation of doubly fed induction motor using high frequency carrier signal injection

Rotor speed estimation of doubly fed induction motor using high frequency carrier signal injection

A speed and flux estimation method of induction motor using fuzzy extended kalman filter

Estimation of Rotor Flux using Neural Network Observer in Speed Sensorless Induction Motor Drive

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