COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) SIMULATION OF IGBT BASED SPEED CONTROL SYSTEM FOR INDUCTION MOTOR USING FUZZY LOGIC

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1 International INTERNATIONAL Journal of Electronics JOURNAL and Communication OF ELECTRONICS Engineering & Technology AND (IJECET), COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN (Print) ISSN (Online) Volume 4, Issue 3, May June, 2013, pp IAEME: Journal Impact Factor (2013): (Calculated by GISI) IJECET I A E M E SIMULATION OF IGBT BASED SPEED CONTROL SYSTEM FOR INDUCTION MOTOR USING FUZZY LOGIC Abhijit D. Ghorapade 1, Snehal S. Mule 2 1 PG Student of Sinhgad College of Engineering, Pune University of pune, India. 2 PG Student of DKTE S TEI, Ichalakaranji, Shivaji University Kolhapur, India. ABSTRACT This research paper presents modeling and simulation of fuzzy logic based speed control system for induction motor using PWM Insulated Gate Bipolar Transistor (IGBT) inverter as a switching device. The model is implemented on computer simulation using MATLAB/Simulink software with different Block Sets. Fuzzy Logic Controller is developed using fuzzy logic toolbox. The control design includes some rules. These rules show a good relationship between two inputs and an output, all of which are nothing but normalized voltages. The inputs to the controller are speed error, derivative of speed error means Change of error, and the output is Change of control. The errors are evaluated according to the rules in accordance to the defined membership functions. The membership functions and the rules have been defined using the FIS editor. The obtained Simulation results of MATLAB/Simulink in relation with electromagnetic torque and speed are given for effectiveness of the study. Key words: Electromagnetic torque, Fuzzy Logic Controller, IGBT, PWM, membership functions. 1. INTRODUCTION The use of Induction Motors (IM) has increased significantly from the day of its creation. They are mainly used in various industrial processes, robotics, house appliances and other similar applications. The reason for its amassed popularity is its robust construction, design simplicity and cost. [1]-[3] Induction motors are more reliable than DC motors. Now days speed control of induction motor is very important because they are operated at different speed. Depending on application its speed will be selected. But the control of IM is complicated due to its nonlinear behavior and its parameters changes with 282

2 operating conditions. [4, 5] Hence in the last few years it has been studied, and various methods for the same have been developed. The Volts/Hertz control scheme is popular because it offers a wide range for speed control with good running performance.[5, 6] Pulse Width Modulation (PWM) with flexible speed drive systems are mostly used in industrial applications for controlling where greater performance is desired. A number of Pulse width modulation systems are used for variable voltage and frequency supply requirement. Today there is a huge development in power electronics and semiconductor technology. This lead improvement in power electronic systems. Therefore different power circuits like inverters have become popular. [7]-[9] Variable voltage and frequency supply is achieved from inverter. Recently IGBT PWM inverters are widely used inverters, because of their improvement in harmonic quality at the output as compared to the other inverters. Now GTO devices are replaced by IGBTs because of their fast evolution in voltage and current ratings and higher switching frequency. [8, 9] In the last few years, Fuzzy logic is popular in motor control applications due to its non-linearties handling ability and independence of the plant modeling. The fuzzy logic controller (FLC) operates in a knowledge-based way. Its control performance is less affected by system parameter variations. [9, 10] A fuzzy logic system uses linguistic if then rules based on systems qualitative aspects and expert knowledge. This paper therefore proposes a simple alternative method for controlling speed of induction motor with PWM IGBT inverter, it uses only fewer values, rules, and decisions making. These rules are Linguistic, not numerical and cover great complexity. This paper is organized as follows. In section 2 the aspects of IGBT PWM Inverter, V/f control strategy and its operational principle is presented. In section 3 the block diagram of system is described in detail. In section 4 design of FLC using membership function and FIS system is explained. In section 5 Simulink model of fuzzy logic controllerand simulation results are discussed. In section 6 the main conclusions are outlined. 2. PULSE WIDTH MODULATION IN INVERTERS 2.1 IGBT PWM Inverter For speed control of induction motor output voltage of an inverter must be adjusted by exercising a control within the inverter. Therefore this paper uses most effective method a pulse-width modulation control in inverter. In this method, a fixed dc input voltage is given to the inverter and a controlled ac output voltage is obtained by adjusting the on and off periods of the inverter components. This is the simplest method of controlling the output voltage. This method is termed as Pulse-Width Modulation (PWM) Control. [11]-[14] Generally power bipolar transistors and MOSFET S are used for inverters to driving ac motors. An alternative inverter, insulated-gate-bipolar transistors (IGBT s) is developed recently. [14] IGBT s offers low ON resistance and need small gate drive power. A gatedrive circuit provides fast turn-on and turn-off, and adequate protection against overload/shoot through faults presented in IGBT. Snubbers are required to limit the stresses on the IGBTs at turn-on and turn-off. [14, 15] The IGBT Inverter combines the properties of both MOSFET'S and bipolartransistors. It gives low saturation voltage and its voltage driven input. It uses very little drive power. It has two control terminals gate and emitter. The IGBT will turn-on when a voltage V GE greater than gate-emitter threshold voltage V GEth is applied between the gate and emitter. To turn-off the IGBT a resistance R GE is connected between gate and emitter, which provides a discharge path for the gate-to emitter capacitance. The IGBT shows a large current fall time 283

3 ISSN (Print), ISSN (Online) Volume 4, Issue 3, May June (2013), IAEME at turn-off. The fall time consistss of two different intervals, one of which is constant and the other is depends on R GE. For smooth switching and to reduce the switching losses IGBT uses some additional networks. [15, 16] Fig1: Power circuit Topology for IM The configuration for IGBT PWM Inverter is shown in Fig.1. Normally, the voltage for the auxiliary winding is higher than that for main winding. The sinusoidal PWM signal, which is responsible for driving the IGBT inverter, was generated by Discrete PWM Generator. 2.2 V/f Control Strategy of Induction Motor The induction motor s speed- toque characteristic states that the induction motor draws rated current and provides the rated torque at the base speed.[17]-[19] The relation of Induction motor is given by following equation. Stator Voltage (V) Stator Flux (φ) x Angular Velocity (ω) V φ x 2πf φ V/f (1) In V/F control, variable frequency signals generated by the PWM control of an inverter and this signal is given to the Motor. In this the V/f ratio is maintained constant to get constant torque for the total operating range. As simply magnitudes of the input variables frequency and voltage are controlled, this is known as scalar control. For this type of controlling little knowledge of the motor is essential. In this type of control the torque developed is load dependent as it is not controlled directly. [18]-[20] The Table.1 show the parameter values used in proposed model. Table 1: Induction Motor Parameters Stator resistance 5Ω Rotor resistance 5Ω Number of pole pairs 2 Stator inductance H Rotor inductance 0.016H Mutual inductance H 284

4 3. BLOCK DIAGRAM OF SYSTEM The Block diagram of proposed system used for speed control of an induction motor is shown in Fig. 2. In the block diagram output of PWM inverter is the reference signal. The current speed of the motor (ω m ) is compared with the reference speed (ω r ), and provides speed error (e). This mechanism is called the feedback mechanism. Change-of-error ( e), that is, the derivative of speed error (e) is computed and both (e) and ( e) are fed to the fuzzifier for fuzzification. The inference system then processes these two fuzzy inputs using the fuzzy control rules and the database, which are defined by the programmer based on the chosen membership function. The obtained fuzzy output is defuzzified by the defuzzifier to give a crisp value, i.e. Change of control (ωsl), and it is applied as a input to the PWM Inverter. The PWM Inverter uses this input to generate a voltage whose frequency and amplitude can be varied by the Fuzzy Logic Controller itself via the above mentioned process. The voltage is fed to the auxiliary winding of induction motor which then runs with a speed which tends to follow the desired speed. Fig 2: Block Diagram of Fuzzy Logic Control System 4. DESIGN OF FUZZY LOGIC CONTROLLER 4.1 Designing of Membership functions The design of a Fuzzy Logic Controller (FLC) uses the membership functions. The membership functions are selected such that they cover the whole universe of discourse.the membership functions must be overlap on each other to avoid any kind of discontinuity with respect to the minor changes in the inputs. To accomplish greater control, the membership functions near the zero region must be narrow, and away from the zero region membership functionsare wider which results the faster response to the system. Therefore, the membership functions for input and output variables are adjusted accordingly. After the selection of proper membership functions a rule base is created. It consists of a number of fuzzy If-Then rules according to the behavior of the system. These rules are similar to the human thought process, means here artificial intelligence is provided to the system. 285

5 Fig3: Membership function for input variables speed error (e) Fig 4: Membership function for Change-of-error ( e) The seven linguistic terms are used for two inputs and one output of fuzzy logic controller. these are as follows NB is Negative Big, NM is Negative Medium, NS is Negative Small, ZE is Zero, PS is Positive Small, PM is Positive Medium and PB is Positive Big. Membership functions for both input variables speed error (e) and Change-of-error ( e) are shown in Fig.3 and Fig.4. Total 49 rules are written in the program according to the syntax provided by MATLAB. If- Then Rules used for the design of the Fuzzy Logic Controller are represented as follow: IF (Error is NB) AND (Change in Error is NS)THEN (Change of control is NB) IF (Error is NB) AND (Change in Error is ZE)THEN (Change of control is NM) The table representing the rule base is as shown in Table

6 Table 2: Fuzzy rule table for output (ω sl ) 4.2. FIS System Now day s Fuzzy Inference System (FIS) is magnificently applied for automatic control modeling. Mamdani s fuzzy inference method is the most commonly used methodology.[21, 22] There are five parts of the fuzzy inference process first is fuzzification of the input variables, second is application of the AND operator to the antecedent, Third is implication from the antecedent to the consequent, Fourth is aggregation of the consequents and fifth is defuzzification. 5. SIMULATION MODEL OF SYSTEM AND SIMULATION RESULTS Simulation diagram of proposed system is shown in Fig.5. The model is implemented using MATLAB/Simulink software with the Sim Power System, Simulink and fuzzy logic Block Sets. Fig 5:Simulation diagram of proposed FLC system 287

7 ISSN (Print), ISSN (Online) Volume 4, Issue 3, May June (2013), IAEME Fig 6: Main Winding current of FLC Controller Fig 7: Speed response of FLC Controller Fig 8:Torque response of FLC Controller Fig 9: The Surface Viewer of proposed system 288

8 The plots for speed, current, and torque for Fuzzy Logic Controller were observed and shown in Fig.6, 7 and 8. It can be seen from the above figures that while using the Fuzzy Logic Controller the settling time is less. FLC approaches the new reference speed faster. From the current plot, the same can be achieved. Fig.6 shows in the current plot the current is sinusoidal. But there is a distortion in the envelope before the machine attains steady state. Because at the time of starting the machine goes through the unstable region.fig.7 shows the Fuzzy Logic Controller attains a steady state speed in small period, and, it is very much close to the reference speed.the torque plot is shown in Fig. 8. It illustrate that the Fuzzy Logic Controller gives oscillations during starting period and after that it gives smooth response.this is because motor provides a desirable response after some time as the controller first has to study and then adjust according to the data provided by the user. The Surface Viewer of proposed system is shown in Fig CONCLUSION The performance of proposed fuzzy control system is satisfactory in relation to load torque variations when it achieves the reference speed. By using FLC we can avoid the numerical calculation involved in higher order systems. Fuzzy logic provides artificial intelligence to the controllers. This is not offered by the conventional controllers. After the simulation of the of the block diagram in MATLAB/SIMULINK, it was found that the fuzzy logic controller used in the simulation worked quite effectively. REFERENCES [1] A. Goedtel, I. N. da Silva and P. J. A. Serni, Load torque identification in induction motor using neural networks technique, Electric Power Systems Research, vol. 77, no. 1, pp , [2] M. N. Uddin, T. S. Radwan and M. A. Rahman, Performances of Fuzzy-Logic-Based Indirect Vector Control for Induction Motor Drive, IEEE Trans. Ind. Applications, Sep/Oct, 2002, pp [3] J. Hsu, J. Kueck, M. Olszewski, D. Casada, P. Otaduy, and L. Tolbert, Comparison of induction motor field efficiency estimation methods, IEEE Trans. Ind. Appl., vol. 34, no. 1, pp , Jan./Feb [4] B. Lu, T. G. Habetler and R. G. Harley, A survey of efficiency estimation methods for in-service induction motors, IEEE Transactions on Industry Applications, vol. 42, no. 4, pp , [5] S. Maiti, C. Chakraborty, Y. Hori and M.C. Ta, Model reference adaptive controllerbased rotor resistance and speed estimation techniques for vector controlled induction motor drive utilizing reactive power, IEEE Transactions on Industrial Electronics, vol. 55, no. 2, pp , [6] B.K.Bose, Power Electronics and Motor Drives-Recent Technology Advances, Proceedings of the 2002 IEEE International Symposium on Industrial Electronics, Vol.1, pp.22 25, [7] B.K.Bose, Expert System, fuzzy Logic, and Neural Network Applications in Power Electronics and Motion Control Proceedings of the IEEE, Vol.82, pp , Aug [8] G. El-Saady, A. M. Sharaf, A. Makky, M. K. Sherbiny and G. Mohamed, A high performance induction motor drive system using fuzzy logic controller, Proceedings of 7th Mediterranean Electro technical Conference, vol. 3, pp ,

9 [9] N. Islam, M. Haider and M. B. Uddin, Fuzzy logic enhanced speed control system of a VSI-fed three phase induction motor, Proceedings of 2nd International Conference on Electrical and Electronics Engineering, pp , [10] V.Chtra, and R.S.Prabhakar Induction Motor Speed Control using Fuzzy Logic Controller World Academy of Science, Engineering and Technology [11] BimalK.Bose, Power Electronics And Variable Frequency Drives, New York, IEEE Press, [12] Vrandaparkhi, FPGA Implementation of PWM control Technique for three phase Induction motor Drive, First International conference on Emerging trends in Engineering and Technology, DOI ICETET , pp [13] Bimal K. Bose, Modern Power Electronics and AC Drives, Pearson education. [14] Leonhand.W, Control of Electrical Drives, Springer Verlag, [15] Wei-Feng Zhang and Yue-Hui Yu, Comparison of Three SVPWM Strategies, Journal of electronic science and technology of china, No.3, pp , Sep [16] Y. El-Ibiary, An accurate low-cost method for determining electric motors s efficiency for the purpose of plant energy management, IEEE Trans. Ind. Appl., vol. 39, no. 4, pp , Jul/Aug [17] Pundaleek. B. H., Manish G. Rathi, Vijay Kumar M. G. Speed Control of Induction Motor: Fuzzy Logic Controller v/s PIController, IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.10, October 2010 [18] J.DengL.Tu., Improvement of direct torque control low-speed performanceby using fuzzy logic technique, Proceedings of IEEE International Conference on Mechatronics and Automation, pp , [19] Alfredo Munoz Garcia, Thomas.A.Lipo, Donald. W.Novotony, A New Induction motor V/f control method capable of High performance Regulation at low speeds, IEEE Transaction on Industry Applications, Vol. 34, No. 4, pp , [20] Gupta.A, Simulation of Variable speed squirrel case Induction motor constant Volts/Hertz operation, IEEE International on Electric Machines and Drives conference Record, Pp TB3/8:1-TB3 18.3, [21] MATLAB/SIMULINK version 2009a, The MathWorks Inc., USA. [22] Fuzzy Inference Systems reference manual for MATLAB/SIMULINK version 2009a. [23] M.Gowrisankar and Dr. A. Nirmalkumar, Implementation & Simulation of Fuzzy Logic Controllers for the Speed Control of Induction Motor and Performance Evaluation of Certain Membership Functions, International Journal of Electrical Engineering & Technology (IJEET), Volume 2, Issue 1, 2011, pp , ISSN Print: , ISSN Online: [24] Vaibhav B. Magdum, Ravindra M. Malkar and Darshan N. Karnawat, Study & Simulation of Direct Torque Control Method for Three Phase Induction Motor Drives, International Journal of Electrical Engineering & Technology (IJEET), Volume 2, Issue 1, 2011, pp. 1-13, ISSN Print: , ISSN Online: [25] Deepak Kumar Goyal, Simulation of Direct Torque and Flux Control Strategy for an Induction Motor Using Matlab/Simulink Software Package, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 1, 2013, pp , ISSN Print: , ISSN Online:

10 AUTHORS DETAIL Abhijit D. Ghorapade was born in Sangli, Maharashtra, in He graduated in Electronics and Telecommunication Engineering at the Shivaji University Kolhapur, Maharashtra, in He is a post graduate student of SCOE Pune from the University of Pune Maharashtra, His research interests are within the fields of Fuzzy systems, embedded systems and control. Snehal S. Mule was born in Sangli, Maharashtra, in She graduated in Electronics and Telecommunication Engineering at the Solapur University Solapur, Maharashtra, in She is a post graduate student of DKTE S TEI Ichalakaranji from the Shivaji University Kolhapur, Maharashtra, Her research interests are within the fields of intelligent systems, Power Electronics and control. 291