Sliding Mode MRAS Speed Sensor less Vector Control for Submersible Motor

 ABSTRACT:

In consideration of the difficulty to install speed sensor result from special high temperature working environment of submersible motor, in this paper, a method of sliding mode model reference adaptive observer(SMMRAS) is used to estimate the speed of sensor less vector controlled submersible motor. This method combines variable structure control with model reference adaptive system (MRAS) to improve the accuracy of speed identification, and the stability and speediness capability of the system are proved by Lyapunov theory. The model of the speed-sensor less vector control system of induction motor is built by MatLab/Simulink. Theoretical analysis and the MATLAB simulation results show that the proposed method used in the system for speed identification has rapid response, and the static and dynamic performance is also perfect

.

KEYWORDS:

1.      Submersible motor

2.      Speed sensor less

3.       Model reference adaptive system

4.      MRAS

5.      Speed estimation

SOFTWARE: MATLAB/SIMULINK

CONTROL SYSTEM:

Fig. 1. Speed identification scheme based upon MRAS

Fig. 2. Speed identification scheme based upon SM MRAS

EXPECTED SIMULATION RESULTS:

a)       Actual speed and estimated speed    

                 

                         

                                                          b) Torque response

Fig. 3Speed and torque curve when load increasing

                                                                        

                                       

                                  (a)    Actual speed and estimated speed  

                       

                              

                                                      (b) Torque response                                                                                                             

Fig4. Speed and torque curve of starting and braking

CONCLUSION:

In this paper the sliding mode speed observer is established. Stability  conditions of a model convergence is introduced by the Lyapunov stability theory. Use the space vector pulse width modulation (SVPWM) technology make the voltage control signal of motor is better optimization. Siding mode speed observer is to reduce the influence of parameters on the system, and to improve the accuracy of the speed identification. In this paper the method can better to achieve the speed identification of motor, has robustness to the parameter changes, can quickly follow the actual rational speed changes. Simulation results were given in the transient and steady states for various operating condition. The simulation results verify that the proposed control schemes provide good dynamics performance in tracking accuracy and disturbance rejection

REFERENCES:

 [1] Wang Y N, Wang H, Qiu S H, et al. The field-oriented control for speed-sensor less induction motor drive based on recurrent fuzzy neural network[J]. Proceedings of the CSEE,2004,24(5):84- 89(in Chinese).

[2] Su W F,Liu C W,Sun X D,et al.Speed controller for induction motors based on kalman filtering[J].Journal of Tsinghua University(Science and Technology),2003,43(9): 1202-1205(in Chinese)

.[3] Zhang P F, Peng W D, Liu X G. Control of Induction Motor Based on

Model Reference Adaptive System[J].2011,34(1):197-199.

[4] Deng H, Xue B, Xu D G, Yang Jing. Speed Estimation for Submersible Motor Based on Elman Neural Network[J]. Proceedings of the CSEE,2007,27(24):102-106(in Chinese).

[5] Schauder C. Adaptive speed identification for vector control of induction motors without rotational transducers[J].IEEE Transactions on Industry Applications,1992,28(5):1054-1061.

Review of Vector Control Strategies for Three Phase Induction Motor Drive

ABSTRACT:

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.

KEYWORDS:

1.      Field oriented control

2.      Induction motor

3.      Sensor less

4.      Loss minimization algorithm

 SOFTWARE: MATLAB/SIMULINK

 BLOCK  DIAGRAM:

 

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

                             

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

EXPECTED SIMULATION RESULTS:

                                      

       

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

CONCLUSION:

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

REFERENCES:

[1] E. Poirier, M. Ghribi and A. Kaddouri, “Loss Minimization Control of Induction Motor Drives Based on Genetic Algorithms”, IEEE Int. Conf. on Electric Machines and Drives, pp. 475–478, 2001.

[2] B. Kumar, ; Y. K Chauhan and V. Shrivastava, “Performance analysis of induction motor drive with optimal rotor flux for energy efficient operation”, IEEE Int. Conf. on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 319 – 322, 2014.

[3] A.V. Ravi Teja,; C. Chakraborty; S. Maiti and Y. Hori, “A New Model Reference Adaptive Controller for Four Quadrant Vector Controlled Induction Motor Drives”, IEEE Transactions on Industrial Electronics, Vol. 59 , No. 10, pp. 3757 – 3767, 2012

. 4] B. Kumar, ; Y. K Chauhan and V. Shrivastava, “Assessment of a fuzzy logic based MRAS observer used in a Photovoltaic array supplied AC drive”, Frontiers in Energy, Vol. 8, No.1, pp. 81-89, 2014.

[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.

Sensor less Speed Estimation and Vector Control of an Induction Motor drive Using Model Reference Adaptive Control

ABSTRACT:

Now-a-days ,sensor less speed control modes of operation are becoming standard solutions in the area of electric drives. The technological developments require a compact and efficient drive to meet the challenging strategies in operation of the system. This paper provides a speed sensor less control of an Induction motor with a model based adaptive controller with stator current vectors. The purpose of the proposed control scheme is to create an algorithm that will make it possible to control induction motors without sensors. A closed loop estimation of the system with robustness against parameter variation is used for the control approach. A Model Reference Adaptive System (MRAS) is one of the major approaches used for adaptive control. The MRAS provides relatively easy implementation with a higher speed adaptation algorithm. MRAS proposed in this paper owing to its low complexity and less computational effort proposes a feasible methodology to control the speed of an Induction Motor (1M) drive without using speed sensors. Simulations results validate the effectiveness of this technique

KEYWORDS:

                          1. Indirect Field oriented control,

                              2. induction motor drive

                              3. sensor less speed estimation,

                              4. Model Reference Adaptive control.

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 Fig1. Proposed Block Diagram of MRAS based 1M drive using PI controller

 SIMULINK BLOCK DIAGRAM:

Fig2 Overall Simulink model of sensor less control of induction motor using MRAS with PI controller.

 EXPECTED SIMULATION RESULTS:

                                                       

Fig.3 MRAS speed response (w"r= 1500 rpm and no-load)  speed

                                                     

Fig4MRAS response with step changes in reference

Fig. 5. MRAS speed response(w"r=1500 rpm and load of  3 Nm).

                                         

  Fig.6. MRAS speed response with a load of 3Nm

                                   

Fig7. Response of MRAS and Conventional Controller.

CONCLUSION:

The model based control scheme is basically an adaptive control mechanism. The reference model of the proposed system consists of the response to be obtained for the input conditions. The adaptive mechanism continuously monitors the adaptable parameter (speed in this case). The adaptable parameter is continuously subjected to changes based on its deviation obtained by comparing it with the response of the reference model. The speed estimation algorithm in MRAS is computationally less intensive. MRAS is a relatively simple algorithm and hence less sophisticated processing can be employed. MRAS strategy is more robust than the conventional one. This makes it better suited if the drive is to be operated in hostile environments. Owing to less sophisticated processing requirements, MRAS technique costs cheaper and hence overall cost of the drive is reduced. With lower cost and greater reliability without mounting problems, the sensor less vector control schemes have made remarkable developments in electric drive technology. Due to lesser rise time taken by MRAS, this method gives faster steady state response and this scheme has better reliability than the conventional scheme.

REFERENCES:

[I] Teresa Orlowska - Kowalska and Mateusz Dybkowski , "Stator Current based MRAS estimator for a wide range speed Sensor less induction motor drives", IEEE Transactions on Industrial Electronics vo1.51, No. 4, April 2010, pp. 1296 - 1308.

[2] B. K. Bose, Power Electronics and Motor Drives, Pearson Education Inc., Delhi, India, 2003.

[3] M. Rodic and K. Jezernik, "Speed-sensor less sliding-mode torque control of induction motor," IEEE Transactions on Industrial  Electronics, vol. 49, no. I, pp. 87-95, February 2002.

[4] L. Harnefors, M. Jansson, R. Ottersten and K. Pietilainen, "Unified sensor less vector control of synchronous and induction motors," IEEE Transactions on Industrial Electronics, vol. 50, no. 1, pp. 153-160, February 2003.

[5] M. Comanescu and L. Xu, "An improved flux observer based on PLL frequency estimator for sensor less vector control of induction motors ," IEEE Transactions on Industrial Electronics, vol. 53, no.1, pp. 50-56, February 2006.

Investigations on Energy Efficient Sensor less Induction Motor Drive

 ABSTRACT:

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 I analyzed with different operating conditions.

KEYWORDS:

1.Field oriented control,

2.induction motor,

3.sensorless,

4.loss minimization algorithm

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

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

                           

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

 EXPECTED SIMULATION RESULTS:

                                          

        

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

CONCLUSION:

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

 REFERENCES:

[1] E. Poirier, M. Ghribi and A. Kaddouri, “Loss Minimization Control of Induction Motor Drives Based on Genetic Algorithms”, IEEE Int. Conf. on Electric Machines and Drives, pp. 475–478, 2001.

[2] B. Kumar, ; Y. K Chauhan and V. Shrivastava, “Performance analysis of induction motor drive with optimal rotor flux for energy efficient operation”, IEEE Int. Conf. on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 319 – 322, 2014.

[3] A.V. Ravi Teja,; C. Chakraborty; S. Maiti and Y. Hori, “A New Model Reference Adaptive Controller for Four Quadrant Vector Controlled Induction Motor Drives”, IEEE Transactions on Industrial Electronics, Vol. 59 , No. 10, pp. 3757 –

[4] B. Kumar, ; Y. K Chauhan and V. Shrivastava, “Assessment of a fuzzy logic based MRAS observer used in a Photovoltaic array supplied AC drive”, Frontiers in Energy, Vol. 8, No.1, pp. 81-89, 2014.

[5] S.M. Gadoue; D. Giaouris and J.W.Finch, “MRAS Sensor less Vector Control of an Induction Motor Using New Sliding-Mode and Fuzzy- Logic Adaptation Mechanisms”, IEEE Transactions on Energy Conversion, Vol. 25 , No. 2, pp. 394 – 402, 2010. 3767, 2012.