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Tuesday, 16 February 2016

Review of Vector Control Strategies for Three Phase Induction Motor Drive


In this paper a high performance induction motor drive without speed sensor is investigated. The rotor flux oriented indirect vector control scheme is used for obtaining high performance. In order to eliminate the speed sensor, a MRAS based speed estimator is designed for gathering the rotor speed information. Also a simple but effective loss minimization algorithm is integrated to calculate the optimal flux for efficiency improvement of the drive. Complete simulation model is developed in Simulink/MATLAB software. The performance of the developed system is analyzed with different operating conditions.


1.      Field oriented control
2.      Induction motor
3.      Sensor less
4.      Loss minimization algorithm



Fig. 1. Block diagram of sensor less IFOC induction motor drive


Fig. 2. Block diagram of rotor flux based MRAS speed estimator


Fig. 3. Speed, speed error and flux response with constant (rated) flux  


Fig. 4. Speed, speed error and flux response with optimal flux

Fig. 5. Low speed tracking response of the drive with constant (rated) flux                            

 Fig. 6. Low speed tracking response of the drive with optimal flux


Fig. 7. Drive response with step load torque with constant flux mode                                             

Fig. 8. Drive response with step load torque with optimal flux mode


In this paper, developed model of sensor less induction motor drive in Simulink/MATLAB software is investigated. Speed estimator is also developed using rotor flux based MRAS technique for sensor less operation. For efficiency improvement particularly under partial loads a model based loss minimization technique is applied. The drive performance is investigated for constant flux and the optimal flux. Drive shows good performance with the optimal flux under various operating conditions


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[5] S.M. Gadoue; D. Giaouris and J.W.Finch, “MRAS Sensorless Vector Control of an Induction Motor Using New Sliding-Mode and Fuzzy Logic Adaptation Mechanisms”, IEEE Transactions on EnergyConversion, Vol. 25 , No. 2, pp. 394 – 402, 2010.