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MRAS

About: MRAS is a research topic. Over the lifetime, 1562 publications have been published within this topic receiving 18736 citations. The topic is also known as: M-RAs & R-RAS3.


Papers
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Journal ArticleDOI
01 Oct 1989
TL;DR: A model-reference adaptive system (MRAS) for the estimation of induction motor speed from measured terminal voltages and currents is described, achieving moderate bandwidth speed control without the use of shaft-mounted transducers.
Abstract: A model-reference adaptive system (MRAS) for the estimation of induction motor speed from measured terminal voltages and currents is described. The estimated speed is used as feedback in a vector control system, thus achieving moderate bandwidth speed control without the use of shaft-mounted transducers. This technique is less complex and more stable than previous MRAS tacholess drives. It has been implemented on a 30 hp laboratory drive, where its effectiveness has been verified. >

1,064 citations

Journal ArticleDOI
TL;DR: The allowable range of motor-parameter changes is determined, which guarantees the stable operation of the sensorless field-oriented IM drive with this speed and flux estimator, and the stability of the whole drive system is guaranteed.
Abstract: This paper deals with an analysis of the vector-controlled induction-motor (IM) drive with a novel model reference adaptive system (MRAS)-type rotor speed estimator. A stability-analysis method of this novel MRAS estimator is shown. The influence of equivalent-circuit parameter changes of the IM on the pole placement of the estimator transfer function and the stability of the whole drive system are analyzed and tested. The influence of the adaptation-algorithm coefficients of the MRAS-estimator scheme is also tested. The allowable range of motor-parameter changes is determined, which guarantees the stable operation of the sensorless field-oriented IM drive with this speed and flux estimator. Dynamical performances of the vector-control system with the current-type MRAS estimator are tested in a laboratory setup.

387 citations

Journal ArticleDOI
TL;DR: In this article, a sensorless vector-control strategy for an induction generator in a grid-connected wind energy conversion system is presented, which is based on a model reference adaptive system (MRAS) observer to estimate the rotational speed.
Abstract: A sensorless vector-control strategy for an induction generator in a grid-connected wind energy conversion system is presented. The sensorless control system is based on a model reference adaptive system (MRAS) observer to estimate the rotational speed. In order to tune the MRAS observer and compensate for the parameter variation and uncertainties, a separate estimation of the speed is obtained from the rotor slot harmonics using an algorithm for spectral analysis. This algorithm can track fast dynamic changes in the rotational speed, with high accuracy. Two back-to-back pulse-width-modulated (PWM) inverters are used to interface the induction generator with the grid. The front-end converter is also vector controlled. The dc link voltage is regulated using a PI fuzzy controller. The proposed sensorless control strategy has been experimentally verified on a 2.5-kW experimental set up with an induction generator driven by a wind turbine emulator. The emulation of the wind turbine is performed using a novel strategy that allows the emulation of high-order wind turbine models, preserving all of the dynamic characteristics. The experimental results show the high level of performance obtained with the proposed sensorless vector-control method.

296 citations

Journal ArticleDOI
TL;DR: Simulation and experimental results demonstrate the feasibility and effectiveness of the proposed control methods, which shows that the estimation error of rotor position based on the identified permanent magnet flux is limited within a very low level.
Abstract: In order to realize precise rotor position/speed control of ac motor drives under sensorless operation, motor parameters should be online estimated. In this paper, the identification on permanent magnet flux of interior permanent magnet synchronous motor (IPMSM) under position sensorless control is investigated. A rotor-flux-oriented vector control with model reference adaptive system (MRAS)-based rotor position/speed estimation is employed as the basic control strategy for IPMSM drive. An identification scheme based on extended Kalman filter for the permanent magnet flux of IPMSM is proposed. By using this scheme, the identification problems due to low-order state equations of IPMSM can be avoided. Based on these works, the online permanent magnet flux identification of IPMSM with rotor position/speed senseless control is realized. Simulation and experimental results demonstrate the feasibility and effectiveness of the proposed control methods, which shows that the estimation error of rotor position based on the identified permanent magnet flux is limited within a very low level.

277 citations

Journal ArticleDOI
TL;DR: In this paper, a model reference adaptive system (MRAS) observer for the sensorless control of a standalone doubly fed induction generator (DFIG) is presented, which allows the formal design of the MRAS observer of given dynamics and further allows the prediction of rotor position estimation errors under parameter mismatch.
Abstract: This paper presents an analysis of a model reference adaptive system (MRAS) observer for the sensorless control of a standalone doubly fed induction generator (DFIG). The analysis allows the formal design of the MRAS observer of given dynamics and further allows the prediction of rotor position estimation errors under parameter mismatch. The MRAS observer analysis is experimentally implemented for the vector control of a standalone DFIG feeding a load at constant voltage and frequency. Experimental results, including speed catching of an already spinning machine, are presented and extensively discussed. Although the method is validated for a standalone generator, the proposed MRAS observer can be extended to other applications of the doubly fed induction machine.

276 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202360
2022136
202178
202089
2019113
2018107