Implementation of a New MRAS Speed Sensorless Vector Control of Induction Machine

Benlaloui, I.; Drid, Saïd; Chrifi‐Alaoui, Larbi; Ouriagli, M.

doi:10.1109/tec.2014.2366473

Cited by 147 publications

(74 citation statements)

References 26 publications

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“…Sun et al 1 use a neural network inverse speed observer, and a fuzzy observer is made use of in Panda et al 2 Model reference adaptive systems are used for speed estimation in previous studies. [3][4][5][6][7] Other techniques available in the literature involve use of Luenberger observer, 8,9 sliding mode observer, [10][11][12] and extended Kalman filter (EKF). [13][14][15][16][17][18] These methods are found to be good speed estimators at high speed, but they fail to give accurate results at near-zero speeds, which continues to be a challenge even now.…”

Section: Introductionmentioning

confidence: 99%

Real‐time implementation of sensorless indirect field‐oriented control of three‐phase induction motor using a Kalman smoothing‐based observer

Syamkumar

Jayanand

2019

Int Trans Electr Energ Syst

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Summary This paper proposes an indirect vector control scheme of an induction motor, using a Kalman smoothing‐based observer to estimate speed. The observer is used to estimate the stator currents, rotor currents, and rotor mechanical speed. Observers based on conventional extended Kalman filters (CEKFs) depend on past output measurements of a system to predict its state variables at the next instant. In a smoothing‐based observer, some future output measurements are also used to obtain a smoothed estimate of a past instant. The smoothed estimate, thus obtained, is used to predict and correct the states of the next instant. The performance of a CEKF‐based observer largely depends on the proper determination of its measurement and process error covariance matrices. A trial and error method is usually engaged to arrive at these matrices. Smoothing helps to obtain a better state estimate compared with CEKF, with the same covariance matrices used in CEKF, which is obtained by trial and error. The improvement in estimation is mainly in the transient region. Estimates of low and zero speeds also show good improvement over those of CEKF. This betterment accomplished is without much increase in computational load. Experiments are conducted to compare the performance of a speed sensorless indirect vector control system with the observer, based on CEKF and smoothing for the same values of noise covariance matrices. The experiments conducted used various reference speeds, including low and zero speeds. Results show the superiority of the smoothing‐based Kalman observer over CEKF‐based observers.

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Section: Introductionmentioning

confidence: 99%

Real‐time implementation of sensorless indirect field‐oriented control of three‐phase induction motor using a Kalman smoothing‐based observer

Syamkumar

Jayanand

2019

Int Trans Electr Energ Syst

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“…Speed estimation based on the quadrature model reference adaptive system (MRAS) has gained high popularity [11] in sensorless speed control research because of its simple control and superior performance, which was first introduced by Schauer [12]. The flux linkages of the voltage model without a speed variable was used as the reference model of (MRAS), while the flux linkage of the current model including speed was used as the adjustable model.…”

Section: Introductionmentioning

confidence: 99%

Sensorless Speed Control Based on the Improved Q-MRAS Method for Induction Motor Drives

Bao

Wang

et al. 2018

Energies

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For high-power and high-performance speed control system, speed feedback signals are generally required. The employment of sensorless control technology makes the installation of the system easier and lower-cost, while its reliability needs to be improved. The robustness of the improved instantaneous reactive power based on the quadrature model reference adaptive system (MRAS) with respect to the variation of the motor inductance parameter is improved by selecting the appropriate reference model and adjustable model. The improved instantaneous reactive power (Q) based on model reference adaptive system (Q-MRAS) algorithm is studied by small signal analysis, and the stability of the control system is verified by the Routh Stability Criterion. The simulation models and experimental platform for the proposed control are built in the laboratory. The feasibility and superiority are verified by the corresponding simulation and experimental results.Keywords: improved instantaneous reactive power (Q) based on model reference adaptive system (Q-MRAS); sensorless control; small signal analysis, induction motor drives

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“…That is the reason why such methods have been widely applied in the sensorless induction motor control despite the complex computational requirements needed by these methods. Another method is the model reference adaptive system (MRAS), which is a generic approach based on a mathematical model [8]. Within MRAS framework and with the aid of error cost function, the parameters of the model are optimized to match the model outputs with the measured outputs.…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Parameter Estimation Using Sparse Grid Optimization Algorithm

Duan

Živanović

Al-Sarawi

et al. 2016

IEEE Trans. Ind. Inf.

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Inaccurate motor parameters can lead to an inefficient motor control. Although several motor estimation methods have been utilized to estimate motor parameters, it is still challenging to ensure a good level of confidence in the estimation. In this paper, we propose a novel offline induction motor parameter estimation method based on sparse grid optimization algorithm. The estimation is achieved by matching the response of machines mathematical model with recorded stator current and voltage signals. This approach is non-invasive as it uses external measurements, resulting in reduced system complexity and cost. A globally optimal point was found by sampling on the sparse grid, which was created using the hyperbolic cross points (HCPs) and additional heuristics. This has resulted in reducing the total number of search points and provided the best match between the mathematical model and measurement data. The estimated motor parameters can be further refined by using any local search method. The experimental results indicate a very good agreement between estimated values and reference values.

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Implementation of a New MRAS Speed Sensorless Vector Control of Induction Machine

Cited by 147 publications

References 26 publications

Real‐time implementation of sensorless indirect field‐oriented control of three‐phase induction motor using a Kalman smoothing‐based observer

Real‐time implementation of sensorless indirect field‐oriented control of three‐phase induction motor using a Kalman smoothing‐based observer

Sensorless Speed Control Based on the Improved Q-MRAS Method for Induction Motor Drives

Induction Motor Parameter Estimation Using Sparse Grid Optimization Algorithm

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