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.

An Improved SVPWM based Shunt Active Power Filter for Compensation of Power System Harmonics

 ABSTRACT:

Space vector pulse width modulation (SVPWM) has been extensively utilized in the three-phase voltage source inverters (VSI) for the benefit of fixed switching frequency, full utilization of DC bus voltage and superior control. In recent times, SVPWM technique was applied for active power filter (APF) control application, as the APF is nothing but of a current controlled VSI. The conventional SVPWM based APF has high computational burden due to complex trigonometric

calculations and sector identification involved to generate the compensating signal, hence the response time for compensation is slow. In this paper, an improved SVPWM technique based shunt APF is presented based on the effective time concept. The effective time concept eliminates the trigonometric calculations and sector identification, thereby it reduces the computational effort. Simulation results demonstrate the efficacy of the APF with the improved SVPWM based control strategy. The response time for compensation is 0.02sec.

KEYWORDS:

1.      SVPWM

2.       Shunt APF

3.      VSI

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM

            

Figure 1. Configuration of Improved SVPWM based shunt APF

CIRCUIT DIAGRAM:

                       

             

Figure 2. Proposed SVPWM control for APF topology

EXPECTED SIMULATION RESULTS:

                      

            

Figure 3. Simulation results (a) Source voltages, (b) Load currents, (c) Compensated source currents, and (d) Filter currents (APF).

                          

                              

Figure 4.DC Bus voltage of the proposed shunt APF

CONCLUSION:

 In this paper, an improved SVPWM based shunt APF is proposed, which is suitable for digital control realization. This method requires less computation when compared to the conventional SVPWM technique as it eliminates the complex trigonometric calculation and sector identification, The performance of shunt APF with this proposed SVPWM method for harmonic compensation is examined and proved to be worthy where the THD of the source currents was reduced from 24.38% to 4.47% and the response time for harmonic compensation is 0.02 sec.

REFERENCES:

[1] H. Akagi, E. H. Watanabe, and M. Aredes, Instantaneous Power Theory and Applications to Power Conditioning, M. E. El-Hawari, Ed. New York: Wiley, 2007.

[2] Recommended Practice for Harmonic Control in Electric Power Systems, IEEE Std. 519-1992, 1992.

[3] Limits for Harmonic Current Emission, IEC 61000-3-2, 2001.

[4] H. Akagi, “New trends in active filters for power conditioning,” IEEE Trans. Ind. Appl., vol. 32, no. 2, pp. 1312–1332, Nov./Dec. 1996.

[5] F. Z. Peng, “Application issues of active power filters,” IEEE Ind. Appl.Mag., vol. 4, no. 5, pp. 21--30, Sep./Oct. 1998.

Integration Of Solar cells With Power Electronic Converters For Power Generation

 ABSTRACT:

An integration and operation of single phase bidirectional inverter with two buck/boost maximum power point trackers (MMPTs) is provided for dc distribution system. In a dc distribution system a bidirectional inverter is required to control the power flow between dc bus and ac grid, and to regulate the dc bus to the certain range of voltage. A droop regulation mechanism is followed to reduce the capacitor size and to balance the power flow between the dc bus and ac grid. The photovoltaic (PV) array voltage can be vary from 0 to 600V, especially with

thin-film PV panels, the MPPT topology is formed with buck and boost converters to operate at the dc-bus voltage around 380V, reducing the voltage stress of its followed inverter. In this paper

the fuzzy logic technique is used to control the bidirectional inverter for improve overall efficiency of the system and it is designed by using MATLAB/SIMULINK software.

KEYWORDS:

                    1.Bi-directional inverter

                    2. buck/boost MPPTs

                    3. dc distribution system

SOFTWARE: MATLAB/SIMULINK

SIMULINK DIAGRAM:

Figure 1. Overall Simu link Model

EXPECTED SIMULATION RESULTS:

Figure 2. Grid Voltage

Figure 3. dc-bus voltage

Figure 4 Buck/Boost Output Waveform

  

 Figure 5. Real and Reactive power

 CONCLUSION:

A single-phase bi-directional inverter with two buck/boost MPPTs has been designed by using the MATLAB/SIMULINK.A buck/boost inverter can be used for both the step-up and step-down process. The inverter controls the power flow between dc bus and ac grid, and regulates the dc bus to a certain range of voltages. Since the PV-array voltage can vary from 0 to 600 V, the MPPT topology is formed with buck / boost converters to operate at the dc-bus voltage around 380 V, reducing the voltage stress of its followed inverter. Also the controller can on-line check the input configuration of the MPPTs, equally distribute the PVarray output current to the two MPPTs in parallel operation, and switch control laws to smooth out mode transition. In this the fuzzy control technique has been used. Integration and operation of the overall inverter system have been discussed in detail, which contributes to ac grid as well as dc-distribution.

 REFERENCES:

[1] Shih-Ming Chen, Student Member, IEEE, Tsorng-Juu Liang, Senior Member, IEEE, Lung-Sheng Yang, and Jiann- Fuh Chen, Member, IEEE: “A Boost Converter With Capacitor Multiplier and Coupled Inductor for AC Module Applications” IEEE Transactions 2013.

[2] J.-M. Shen, H.-L. Jou, and J.-C. Wu, “Novel transformer less grid connected power converter with negativegrounding for photovoltaic generation system,” IEEE Transactions 2012.

[3] Tamás Kerekes, Member, IEEE, Remus Teodorescu, Senior Member, IEEE, Pedro Rodríguez, Member, IEEE,Gerardo Vázquez, Student Member, IEEE, and EmilianoAldabas, Member, IEEE: “A New High-Efficiency Single- Phase  Transformer less PV Inverter Topology” IEEE Transactions 2011.

[4] Loc Nguyen Khanh, Student Member, IEEE, Jae-Jin Seo, Yun-Seong Kim, and Dong-Jun Won, Member, IEEE: “Power-Management Strategies for a Grid-Connected PV-FC Hybrid System” IEEE Transactions 2010.

[5] T.-F. Wu,K.-H.Sun, C.-L.Kuo, and C.-H. Chang, “Predictive current controlled 5 kW single-phase bidirectional inverter with wide inductance variation for DC micro grid applications,” IEEE Transactions 2010.