Mathematical Modeling and Fuzzy Based Speed Control of Permanent Magnet Synchronous Motor Drive

ABSTRACT:

To design a control system it is desirable to represent the actual system in mathematical form. So a mathematical representation of a permanent magnet synchronous motor is presented here. The inductances of a PMSM vary as a function of rotor position, the d-q model is commonly used to represent PMSM. The d-q model is obtained to implement the current control in rotor reference frame. A fuzzy logic based speed controller for permanent magnet synchronous motor is proposed and investigated. In the paper the dynamic response of PMSM drive with proposed controller is analyzed for different loading conditions and with various speed.

KEYWORDS:

1.     FLC

2.     Mathematical model

3.     PI controller

4.      PMSM

5.     SVM

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

EXPECTED SIMULATION RESULTS:

CONCLUSION:

A mathematical modeling of PMSM is presented here and d-q model is obtained to implement the current control in rotor reference frame. In this paper performance of a FLC is investigated to speed control of PMSM. FLC is designed with three scaling factors (two inputs & one output) for setting the controller parameter according to actual system. Tuning of these scaling factors is done based on the parameter of motor and intervals for which membership functions are defined. Performance of proposed FLC with gain tuning is found good in all operating conditions.

REFERENCES:

 [1] M. Kadjoudj, M. E. H. Benbouzid, C. Ghennai, and D. Diallo, "A robust hybrid current control for permanent-magnet synchronous motor drive," IEEE Transactions on Energy Conversion, vol. 19, pp. 109- 115, 2004.

[2] Y. Baudon, D. Jouve, and J. P. Ferrieux, "Current control of permanent magnet synchronous machines. Experimental and simulation study," IEEE Transactions on Power Electronics, vol. 7, pp. 560- 567, 1992.

[3] B. K. Bose, Modern power electronics and AC drives: Prentice Hall PTR USA, 2002.

[4] R. H. Park, "Two-reaction theory of synchronous machines-II," Transactions of theAmerican Institute of Electrical Engineers, vol. 52, pp. 352-354, 1933.

[5] P. Vas, Sensorless vector and direct torque control vol. 729: Oxford university press Oxford, UK, 1998.

Indirect Vector Control of Induction Motor Using Fuzzy Sliding Mode Controller

ABSTRACT:

The paper presents a fuzzy logic speed control system based on fuzzy logic approach for an indirect vector controlled induction motor drive for high performance. The analysis, design and simulation of the fuzzy logic controller for indirect vector control induction motor are carried out based on fuzzy set theory. The proposed fuzzy controller is compared with PI controller with no load and various load condition. The result demonstrates the robustness and effectiveness of the proposed fuzzy controller for high performance of induction motor drive system.

KEYWORDS:

1.     Indirect vector control

2.     Fuzzy logic control

3.     PI controller

4.     Induction motor

5.     Speed control

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

EXPECTED SIMULATION RESULTS:

CONCLUSION:

This paper has successfully demonstrated the application of the proposed fuzzy sliding mode control system to an indirect field-oriented induction motor drive for tracking periodic commands. First, the description of the classical sliding mode controller (SMC) is presented in detail. Then, the fuzzy logic control is used to mimic the hitting control law to remove the chattering. Compared with the conventional sliding mode control system, the fuzzy sliding mode control system results in robust control performance without chattering. The chattering free improved performance of the FSMC makes it superior to conventional SMC, and establishes its suitability for the induction motor drive.

REFERENCES:

 [1] B.K Bose “Modern power electronics and ac drives “Prentice-Hall Of India, New Delhi, 2008.

[2]R.J.Wai, “Fuzzy sliding-mode control using adaptive tuningtechnique,”IEEETrans.Ind.Elelctron.Vol.54,no.1,pp .586-594,feb2007.

[3] K.B.Mohanty, “Sensorless sliding mode control of induction motor drives,” IEEE Region10 conference, TENCON, Hyderabad, Nov 2008, pp.1-6.

[4] E.Cerruto,A.Consoli,A.Testa,“Fuzzy adaptive vector control of induction motor drives,” IEEE Transaction on Power Electronic, vol.12, no.6, pp.1028-1040, Nov.1997.

[5] K..B.Mohanty, “A fuzzy sliding mode controller for a field-oriented induction motor drive,” Journal of Institution of Engineers (India), vol.86, pp.160-165, Dec.2005.

Indirect Vector Control of Induction Motor

Using Fuzzy Logic Controller

ABSTRACT:

The paper presents a fuzzy logic speed control system based on fuzzy logic approach for an indirect vector controlled induction motor drive for high performance. The analysis, design and simulation of the fuzzy logic controller for indirect vector control induction motor are carried out based on fuzzy set theory. The proposed fuzzy controller is compared with PI controller with no load and various load condition. The result demonstrates the robustness and effectiveness of the proposed fuzzy controller for high performance of induction motor drive system.

KEYWORDS:

1.     Indirect vector control

2.     Fuzzy logic control

3.     PI controller

4.     Induction motor

5.     Speed control

  

SOFTWARE: MATLAB/SIMULINK

 BLOCK DIAGRAM:

EXPECTED SIMULATION RESULTS:

CONCLUSION:

In this paper fuzzy logic controller for the control of an indirect vector-controlled induction motor was described. The drive system was simulated with fuzzy logic controller and PI controller and their performance was compared. Here simulation results shows that the designed fuzzy logic controller realises a good dynamic behaviour of the motor with a rapid settling time, no overshoot and has better performance than PI controller. Fuzzy logic control has more robust during change in load condition.

REFERENCES:

 [1] B.K Bose “modern power electronics and ac drives “Prentice-Hall Publication,Englewood Cliffs,New Jersey,1986

[2] F.BLASCHKE, “The principle of field orientation as applied to the new transvector closed loop control system for rotating-field machine,” Siemens Rev., Vol.34,no.3,pp.217-220,May1972.

[3] M Nasir Udin,Tawfik S.Radwan,and M.Azizur Rahman,“ Performance of Fuzzy-Logic-Based Indirect vector control for induction motor drive,”IEEE Transaction on Industry Applications,vol.38,no.5,September/October 2002.

[4] Z Ibrahim and E.Levi, “A comparative analysis of Fuzzy Logic and PI controller in High-Performance AC machine drives using Experimental Approach,”IEEE Trans.Industry Application,vol.38,no.5,pp1210-1218,Sep/Oct 2002.

[5] Gilberto C.D.Sousa,Bimal K.Bose and John G.Cleland “Fuzzy Logic Based On-Line Efficiency Optimisation Vector-Controlled Induction Motor Drives,”IEEE Trans.Industrial Electronics,Vol.42,pp.192-198,April 1995.

Grid Interactive PV System with Harmonic and Reactive Power Compensation Features using a Novel Fuzzy Logic Based MPPT

ABSTRACT:

Photovoltaic (PV) cell characteristics are highly nonlinear that gives single Maximum Power Point (MPP) on P-V curve under uniform insolation condition. The characteristics and hence MPP point changes with the variation in insolation and temperature. In order to extract a maximum power from PV array, a fuzzy based MPP tracking algorithm is proposed. The algorithm accepts single input that is slope of P-V curve and generates the duty ratio as an output that operates the boost converter to track MPP. The algorithm gives faster convergence by applying variable step in duty ratio and gives accurate MPP. The two stage grid interactive PV system described in this paper supplies active power as well as provides harmonic and reactive power compensation. This additional feature increases the effective utilization of PV inverter and increases the overall efficiency of the system. The simulation results validate the performance and stability of the grid interactive PV system using the proposed algorithm for active current injection as well as harmonics and reactive power compensation.

KEYWORDS:

1.     Photovoltaic system

2.     Maximum power point tracking

3.     Fuzzy logic controller

4.     Harmonic elimination

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

EXPECTED SIMULATION RESULTS:

 CONCLUSION:

In this paper, multi functional grid interactive PV system is presented using a novel fuzzy logic based MPPT. The proposed MPPT controller is able to track the MPP accurately under uniformly varying as well as rapidly changing insolation and gives faster convergence as a variable step size in duty ratio is applied inherently by the algorithm. The proposed fuzzy controller maintains the dc link voltage within the limit for injecting the power into the grid. Apart from injecting active power during day time, the PV inverter also compensates the harmonics and reactive power during day time as well as at night. The current drawn from the grid is sinusoidal and the total harmonic distortion is well below the specified limit in the IEEE-519 standard. The simulation results validate the performance of grid interactive PV system for both active power injection as well as shunt active power filter functionality to mitigate the power quality issues thus increases the utilization factor of the system.

REFERENCES:

 [1] T. Esram and P. Chapman, “Comparision of photovoltaic array maximum power point tracking techniques”, IEEE Trans. on Energy Conversion, vol. 22, No. 2, June 2007.

[2] S. Jain and V. Agarwal , “Comparison of the performance of maximum power point tracking schemes applied to single-stage grid-connected photovoltaic systems”, IET Electr. Power Appl., vol. 1, no. 756(5), pp. 753-762, September 2007.

[3] P. Takun, S. Kaitwanidvilai and C. Jettanasen, “Maximum Power Point Tracking using Fuzzy Logic Control for Photovoltaic Systems”, International conference of engineers and computer scientists (IMECS), Vol.-II, pp. 986-990, March-2011.

Neural Network Based Dynamic Simulation of Induction Motor Drive

ABSTRACT:

With the improvement in the technology of Microprocessor and Power Electronics, Induction motor drives with digital control have become more popular. Artificial intelligent controller (AIC) could be the best candidate for Induction Motor control. Over the last two decades researchers have been working to apply AIC for induction motor drives. This is because that AIC possesses advantages as compared to the conventional PI, PID and their adaptive versions. The main advantages are that the designs of these controllers do not depend on accurate system mathematical model and their performances are robust. In recent years, scientists and researchers have acquired significant development on various sorts of control theories and methods. Among these control technologies, intelligent control methods, which are generally regarded as the aggregation of Fuzzy Logic Control, Neural Network Control , Genetic Algorithm, and Expert System, have exhibited particular superiorities. The artificial neural network controller introduced to the system for keeping the motor speed to be constant when the load varies. The speed control scheme of vector controlled induction motor drive involves decoupling of the speed and ref speed into torque and flux producing components. The performance of artificial neural network based controller's is compared with that of the conventional proportional integral controller. The dynamic modeling of Induction motor is done and the performance of the Induction motor drive has been analyzed for constant and variable loads. By using neuro controller the transient response of induction machine has been improved greatly and the dynamic response of the same has been made faster.

KEYWORDS:

1.      Vector Control (VC)

2.      Direct, Dynamic Simulation

3.      Artificial Intelligence (AI)

4.       PI Controller

5.      Artificial Neural Network (ANN)

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

EXPECTED SIMULATION RESULTS:

 

CONCLUSION:

An Artificial intelligent based vector controlled induction motor has been presented in this paper. The vector control strategy is developed with Neural network controller. The conventional vector control of induction motor is compared with the proposed neural network based controllers, and from the results it is observed that the performance with neural network controller is better than PI controller. In steady state condition, the rise time and speed regulation with conventional controller is more than that of the ANN controller. During transient condition, the settling time before changing the load and after changing the load is less in case of ANN controller as compared to PI controller. It is observed that there is no overshoot in case of ANN controller. Thus, by using neuro controller the transient response of induction machine has been improved greatly and the dynamic response of the same has been made faster.

 REFERENCES:

 [1] K. L . Shi, T . F. Chan, Y. K. Wong and S. L . HO, "Modeling and simulation of the three phase induction motor Using SIMULINK," Int.J. Elect. Enging. Educ., Vol. 36, 1999, pp. 163–172.

[2] Tze Fun Chan and Keli Shi, "Applied intelligent control of induction motor drives," IEEE Willey Press, First edition, 2011.

[3] P.C. Krause, "Analysis of Electrical Machinery and Drives System, "IEEE Willey Press, 200).

[4] Ned Mohan, "Advanced Electric Drives: Analysis, Control Modeling using Simulink,"MNPERE Publication ,2001.

[5] M. Nasir Uddin and Muhammad Hafeez, "FLC-Based DTC Scheme to Improve the Dynamic Performance of an IM Drive," IEEE Trans.on Industry Applications , Vol -48 , No 2, Mar/Apr 2012, pp 823-831.

SPEED CONTROL OF SWITCHED RELUCTANCE MOTOR USING SLIDING MODE CONTROL STRATEGY

ABSTRACT:

A robust speed drive system for a switched reluctance motor (SRM) using sliding mode control strategy (SLMC) is presented. After reviewing the operation of an SRM drive, a SLMC based scheme is formulated to control the drive speed. The scheme is implemented using a micro controller and a high resolution position sensor. The parameter insensitive characteristics are demonstrated through computer simulations and experimental verification.

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

EXPECTED SIMULATION RESULTS:

 CONCLUSION:

This paper has described the application of sliding mode control to SRM drive. Tests were carried out a 5 hp SRM to validate the speed control responses predicted by simulation and to validate the analytical approach. It has been shown that sliding mode control is insensitive to plant parameter variations and that it provides rejection of inherent drive non linearities. The problem of torque chattering is overcome by adopting a cascaded integral operation in the torque control path between the sliding mode controller and the feed forward controller.

 REFERENCES:

 1. P.J.Lawrenson, J.M. Stephenson, P.T. Blenkisop, J. Corda and N.N. Fulton, "Variable-speed switched reluctance motors", IEE Proc., pt. B, vol. 127, no. 4, pp. 253-265, July 1980.

2. T.J.E. Miller, "Converter volt-ampere requirements of the switched reluctance motor drive", IEEE IAS Annual Meeting Conf. Record, Chicago, pp. 813-819, Oct. 1984

3.B.K. Bose, T.J.E. Miller, P.M. Szczesny and W.H. Bicknell, "Microcomputer control of switched reluctance motor", IEEE US Annual Meeting Conf. Record, Toronto, pp. 542-547, Oct. 1985.

4.A.R Oza, R. Krishnan and S . Adkar, "A microprocessor control scheme for switched reluctance motor drives", Conf. Record, IEEE Industrial Electronics Conference, pp.

5.V.I. Utkin,"Variable structure control systems with sliding mode", IEEE Trans. on Auto. Control, Vol. AC-22, No. 2, pp. 210-222, April 1977.

Analysis of the Operation of a D-STATCOM in Unbalanced Distribution Systems Under Voltage Disturbances

ABSTRACT:

In this paper, a study on the operation of a static synchronous compensator for distribution systems, DSTATCOM, in relation to its performance in the presence of events that can affect system´s power quality, is presented. Performance under short-circuit and load variation is assessed. The IEEE 13 node test feeder, which is a highly loaded unbalanced system, is used in the study. The D-STATCOM consists mainly of a three-level inverter with IGBTs and PWM control. Modeling and simulations are implemented in Matlab/Simulink.

KEYWORDS:

1.      D-STATCOM

2.      Power Quality

3.      Unbalanced Distribution Systems.

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

EXPECTED SIMULATION RESULTS:

CONCLUSION:

This work presented simulation results for a D-STATCOM operating in a typical unbalanced distribution test system. Simulations were conducted in order to assess the DSTATCOM performance under balanced and unbalanced disturbances. A commercially available D STATCOM topology was chosen. In addition, a standard control system was used, in order to illustrate that, in general, good solutions for power quality related problems can be obtained without resorting to more complex and specific problem-oriented control systems. The DC voltage, although not shown, was in all cases, stable with low ripple. Controller parameters did not have to be modified for different situations in order to perform satisfactorily. Finally, it can be concluded that the D-STATCOM used in this study can contribute significantly to the improvement of power quality in unbalanced distribution systems.

REFERENCES:

[1] N. G. Hingorani, and L. Gyugyi, Understanding Facts: Concepts and Technology of Flexible AC Transmission Systems. Wiley-IEEE Press, December 1999.

[2] P. Giroux, G. Sybille, and H. Le-Huy, “Modeling and Simulation of a Distribution STATCOM using Simulink´s Power System Blockset,” in IECONOl: The 27th Annual Conference of the IEEE Industrial Electronics Society, pp. 990-994.

[3] C. Hochgraf, and R. H. Lasseter, “Statcom Controls for Operation with Unbalanced Voltages,” IEEE Transactions on Power Delivery, vol. 13, No. 2, April 1998.

[4] M. T. Bina, and M. D. Eskandari, “Consequence of Unbalance Supplying Condition on a Distribution Static Compensator,” in 35^th Annual IEEE Power electronics Specialists Conference, pp. 3900-3904.

[5] B. Blažič, and I. Papič, “Improved D-StatCom Control for Operation With Unbalanced Currents and Voltages,” IEEE Transactions on Power Delivery, vol. 21, No. 1, January 2006.