Comparative Analysis of Two Speed-Estimation Methods for Dual Three-Phase Induction Motor with Stator Resistance Online Identification

The dual three-phase induction motor (DTPIM) has gained wide attention in special applications, such as vessel propulsion, because of its advantages of less torque ripple and higher reliability. However, speed sensors are greatly affected and easily become faulty when used in harsh environments for a long time. In this paper, two model reference adaptive system (MRAS) speed-estimation methods are proposed, based on the double (α, β) coordinate system (DCS) and vector space decomposition method (VSD) of the two groups of the three-phase armature vectors, respectively. Both methods can be used for the speed sensorless control system of the DTPIM to improve reliability. The changing of the stator resistance value, caused by temperature variation, affects the accuracy of the speed-estimation. Two online resistance-identification algorithms, combining the DCS method and the VSD method, were proposed to reduce the effect of changes in stator resistance. Simulation results show that the dynamic speed-estimation error of the VSD method decreased greatly compared with the DCS method, which verifies the effectiveness of the theoretical analysis.

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Error Compensation of Stator Resistance Estimation in Sensorless Induction Motor Drives

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.241-244.1812 ◽

2012 ◽

Vol 241-244 ◽

pp. 1812-1815

Author(s):

Zi Cheng Li ◽

Zhou Ping Yin ◽

Li Zeng

Keyword(s):

Induction Motor ◽

Error Compensation ◽

Estimation Error ◽

Motor Drives ◽

Speed Estimation ◽

Motor Speed ◽

Induction Motor Drives ◽

Stator Current ◽

Stator Resistance ◽

Model Reference Adaptive

This paper proposed a method of stator resistance estimation for sensorless induction motor (IM) drives based on a model reference adaptive system (MRAS). In this scheme, the error between estimated stator current and real stator current is regarded as the system error to estimate the rotor speed. Because of the motor parameters vary with inner temperature of the motor, the influence of motor speed estimation due to stator resistance identification error was analyzed. The error compensation method for stator resistance estimation was proposed. The algorithm is given to compensate the stator resistance deviation, which results in the speed estimation error. Simulation and experimental results show the good performance for the proposed scheme in speed and robustness for sensorless induction motor drives.

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Effect of Parametric Variations and Voltage Unbalance on Adaptive Speed Estimation Schemes for Speed Sensorless Induction Motor Drives

International Journal of Power Electronics and Drive Systems (IJPEDS) ◽

10.11591/ijpeds.v6.i1.pp77-85 ◽

2015 ◽

Vol 6 (1) ◽

pp. 77 ◽

Cited By ~ 1

Author(s):

Mohan Krishna. S ◽

Febin Daya. J.L

Keyword(s):

Induction Motor ◽

Constant Load ◽

Motor Drives ◽

Speed Estimation ◽

Induction Motor Drives ◽

Speed Sensorless ◽

Machine Model ◽

Voltage Unbalance ◽

Space Harmonics ◽

Stator Resistance

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper analyses the effect of incorrect setting of parameters like the stator resistance, rotor time constant, load torque variations and also Voltage unbalance on various adaptive control based speed estimation techniques fed from the machine model. It also shows how the convergence mechanisms of the adaptation schemes are affected during these conditions. The equivalent models are built and simulated offline using MATLAB/SIMULINK blocksets and the results are analysed.

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Rotor Speed and Stator Resistance Identification of Direct Torque Control for Induction Motor Based on Mutual MRAS

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.383-390.2458 ◽

2011 ◽

Vol 383-390 ◽

pp. 2458-2463

Author(s):

Zhi Ting She ◽

Jun Bo Yuan ◽

Yong Zheng ◽

Yong Jin Peng

Keyword(s):

Induction Motor ◽

Direct Torque Control ◽

Torque Control ◽

System Stability ◽

Rotor Speed ◽

Speed Sensorless ◽

Model Reference ◽

Stator Resistance ◽

Model Reference Adaptive ◽

Speed Identification

Based on the dynamic mathematical model of induction motor, the mutual model reference adaptive system method (MRAS) for rotor speed identification is proposed to implement a speed sensorless direct torque control of induction motor. The model reference adaptive theory is flexibly used in the rotor speed and the stator resistance online identification. The reference model and adjustable model used in the mutual MRAS scheme are interchangeable. Therefore, The induction motor speed sensorless direct torque control system can obtain high-precision speed identification. Computer simulations and experimental results show that the method can solve the problems of speed control accuracy and system stability under the influence of motor parameter variation. The low speed performance of DTC is also improved.

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Speed Sensorless Control System Based on MRAS

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.742.586 ◽

2015 ◽

Vol 742 ◽

pp. 586-589

Author(s):

Zhi Jiao

Keyword(s):

Control System ◽

Sensorless Control ◽

Speed Estimation ◽

Rotor Speed ◽

Speed Sensorless ◽

Motor Control System ◽

Estimation Precision ◽

Identification Theory ◽

Stator Resistance ◽

Model Reference Adaptive

In this paper, a strategy for estimating the induction motor’s rotor speed is proposed. The proposed rotor speed estimation strategy is based on model reference adaptive identification theory. By applying the proposed strategy, the induction motor control system can estimate the induction motor's rotor speed precisely. To improve the rotor speed estimation performance of the system, two methods have been adopt. The speed sensorless control system based on proposed strategy was built with Simulink blocks in Matlab platform. The corresponding simulation results demonstrate that the proposed method can operate stably in the whole range of speed with preferable estimation precision of stator resistance and rotor speed.

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Speed estimation of faulty three-phase induction motor using model reference adaptive system

2015 IEEE Conference on Energy Conversion (CENCON) ◽

10.1109/cencon.2015.7409572 ◽

2015 ◽

Cited By ~ 2

Author(s):

A. Monadi ◽

M. Jannati ◽

N.R.N Idris ◽

S. E. Alavi

Keyword(s):

Induction Motor ◽

Adaptive System ◽

Speed Estimation ◽

Model Reference ◽

Model Reference Adaptive System ◽

Three Phase ◽

Model Reference Adaptive

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Three-Phase Induction Motor Speed Estimation Using Recurrent Neural Network Structure

Journal of Electronic Voltage and Application ◽

10.30880/jeva.2021.02.02.003 ◽

2021 ◽

Vol 2 (2) ◽

Author(s):

Adam Islam Ridhatullah ◽

◽

Ariffuddin Joret ◽

Iradiratu Diah Prahmana Karyatanti ◽

Asmarashid Ponniran ◽

...

Keyword(s):

Neural Network ◽

Induction Motor ◽

Recurrent Neural Network ◽

Speed Control ◽

Prototype System ◽

Speed Estimation ◽

Motor Speed ◽

Speed Sensorless ◽

Speed Sensor ◽

Three Phase

In induction motor speed control method, the development of the field-oriented control (FOC) algorithm which can control torque and flux separately enables the motor to replace many roles of DC motors. Induction motor speed control can be done by using a close loop system which requires a speed sensor. Referring to the speed sensor weaknesses such as less accurate of the measurement, this is due to the placement of the sensor system that is too far from the control system. Therefore, a speed sensorless method was developed which has various advantages. In this study, the speed sensorless method using an artificial neural network with recurrent neural network (RNN) as speed observer on three-phase induction motor has been discussed. The RNN can maintain steady-state conditions against a well-defined set point speed, so that the observer is able and will be suitable if applied as input control for the motor drives. In this work, the RNN has successfully estimated the rotor flux of the induction motor in MATLAB R2019a simulation as about 0.0004Wb. As based on speed estimation error, the estimator used has produced at about 26.77%, 8.7% and 6.1% for 150rad/s, 200rad/s and 250rad/s respectively. The future work can be developed and improved by creating a prototype system of the induction motor to get more accurate results in real-time of the proposed RNN observer.

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