 Open Access
 Total Downloads : 522
 Authors : Venkat Ramana Reddy Anchuri, Swarna Enugala, Raghavendar Inguva
 Paper ID : IJERTV2IS120115
 Volume & Issue : Volume 02, Issue 12 (December 2013)
 Published (First Online): 18122013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Performance Analysis of Indirect Vector Controlled Induction Motor Drive Using PI & Fuzzy Logic Controllers
Venkat Ramana Reddy Anchuri1, Swarna Enugala2, Raghavendar Inguva3
1PG Student, Department of Electrical & Electronics Engineering, TKR Engineering College, Hyderabad, AP, India 2 Asst. Prof, Department of Electrical & Electronics Engineering, CMR Engineering College, Hyderabad, AP, India 3Assoc. Prof, Department of Electrical & Electronics Engineering, TKR Engineering College, Hyderabad, AP, India
ABSTRACT
This paper proposes an effective control strategy for Indirect vector control of Induction motor using Fuzzy Inference System (FIS). The steady state & dynamic performance of the induction motor drive can be considerably improved with the proposed strategy. The Fuzzy Inference System (FIS) built with two inputs (error & change in error) & one output (command signal). This scheme doesnt require algorithms for estimation of rotor resistance when drive gets heated up which is the case with conventional PI control. The drive performance proves to be equally good even when the rotor resistance deviates from the actual value due to motor heating. The performance of the drive at different load torques & reference speeds with FIS controller against the conventional PI controller shows the effectiveness of the proposed scheme. The entire simulation study is performed using MATLAB/Simulink software using basic building blocks.
Keywords:
Fuzzy Inference System, PI controller, Fuzzy logic controller, Indirect Vector Control of Induction Motor

INTRODUCTION
Induction motors are being widely used in the industries due to their ruggedness & simplicity. Due to the advent of power electronics in late 1970s it made possible to control the induction motor speed & torque more precisely as per the load requirement without compromising the drive
efficiency. However the design engineers still face challenges due to nonlinearity & complex mathematical model of the induction motor. For high performance applications the motor speed should closely follow the reference disregarding load disturbances & machine parameter variations. Traditionally the speed control of induction motor is achieved using fixed PID gains. But the fixed gain controllers are very sensitive to parameter variations, load disturbances etc. Thus, the controller parameters have to be continuously adapted [main]. The development exact mathematical model of induction motor solves the problem of having fixed gain controllers to some extent. However it is often difficult to develop an accurate mathematical model due to nonlinearity, unknown load variation and unavoidable parameter variations due to temperature variations and system disturbance. The stated problems can be overcome using, Fuzzy logic controller (FLC) for motor control purpose. The advantage of FLC compared to conventional PI controller is the designer is relieved from need of having plant mathematical model. FLC is based on linguistic rules within IFTHEN general structure, which is the basic of the human logic [3].
In this paper the performance analysis of conventional PI & Fuzzy logic controllers applied to control speed of Induction motor using Indirect vector control is presented. The performance of the drive system with these controllers is extensively studied & results show that ANFIS controllers perform superior compared to other controllers.

INDIRECT VECTOR CONTROL
The indirect vector control method is essentially same as the direct vector control except the unit
Slip frequency can be calculated as
= . (5)
vector is generated in an indirect manner using the measured speed r and slip speed sl. The
For constant rotor flux r
and dr =
dt
following dynamic equations are taken into consideration to implement indirect vector control strategy [1].
e = e dt …….(1)
0 substituting in equation (4) yields the rotor flux set as
r = Lm ids………………………………………… (6)
The electromechanical torque developed is given by
+
… (2)
= 3
… (7)
2
2
+
(3)
From equations (6) & (7) reference current is* is
generated as shown in the Vector control
For decoupling control
= 0, So the total flux
subsystem block in Figure.1. On the other hand
r directs on the de
qr
axis.
Torque reference Te* is calculated with respect to the machine operating conditions(whether motoring or generating) using the flux controller
Now from equations (1) and (2) we get,
+ = … (4)
subsystem shown in Figure.1.
Figure.1 Simulink implementation of indirect vector control using PI Controller
The main principle of indirect vector control lies in the fact, how the rotor position is calculated. In case of direct torque control rotor position is evaluated using Hall Effect sensors embedded in stator winding. In case of indirect vector control the rotor position is evaluated by calculating the slip speed sl, adding it to the current rotor speed r & taking the integral of the
synchronous speed e calculated. The Simulink blocks shown in the Figure.1 also illustrates the calculation of rotor position e.
The complete Simulink model of indirect vector control using PI controller is shown in Figure.2.
Figure.2 Simulink implementation of indirect vector control using PI Controller
The reference current is* generated is decomposed into 3 currents with respect to the rotor position e calculated as shown in Ref iabc subsystem in Figure.2. The PWM inverter subsystem generates 3 voltages so as to control
motor speed & torque with respect to reference speed & load torque.

Simulation Results with PI
Figure.3 Torque developed for different load torques using PI Controller
Figure.4 Reference & actual speed signals with PI controller
Test Scenario: The reference speed is set to 1200 rpm for t = 0 to 4secs 1440 rpm for t = 4 to 8secs. Load torque set to 15 Nm for t= 0 to 2secs, 25 N m for t = 2 to 6secs and 35 Nm for t = 6 to 8secs
The plots for Torque in Figure.3 & Speed in Figure.4 prove the response of the drive is effective with very less steady state error. This kind of drive behavior is important where the machine speed has to stay constant irrespective of the load torque (or external disturbance) & control of speed as specified by the user. The electromagnetic torque developed by the induction motor with respect to the reference is almost instantaneous as shown in the plot.

Simulations Results with FLC
The same test scenario is implemented by replacing the PI controller with Fuzzy logic controller. FLC is designed using the MATLAB Fuzzy logic toolbox. The FLC controller has two inputs error & change in error & produces output.
About 49 rules are created with respect to the membership functions created for error & change in error. Sugeno type fuzzy inference system is selected to process the error & change in error and produces crisp output. Figure.5 shows the plot between error change in error & Fuzzy controller output.
Figure.5 Plot between error, change in error & Fuzzy controller output
Figure.6 Complete Simulink model of Fuzzy logic Controller based Indirect Vector Control of Induction Motor
Figure.7 Reference & actual torques signals with fuzzy logic controller
Figure 8 Reference & actual speeds with fuzzy logic controller

PI Controller response to Parameter Variation
Indirect vector control of induction motor performance with conventional PI controller showed desirable response under change in
reference speed & load torques. The controller response is quite good under varying loads, however PI controllers are very sensitive to system parameter variations, and any change in machine parameters results in unsatisfactory control over speed & electromagnetic torque generated as shown in plots. The gains have to be readjusted to obtain better control over speed & torque.
Figure.9 Speed response using PI controller with change in machine resistance

Fuzzy Logic Controller response to Parameter Variation
On the other hand Fuzzy logic controller also gave quite good performance over speed & load torque changes & performs well under dynamics. Fuzzy logic controller outperforms PI controller when machine parameters change, for instance,
when induction motor is loaded, the rotor resistance of the motor increases due to heating. This increase in rotor resistance results in uncontrolled system with PI controller. Fuzzy logic controller is very little affected with this change & gives equally good performance with respect to normal working conditions.
Figure 10 Torque response using Fuzzy logic controller with change in machine resistance
Figure.11 Speed response using Fuzzy logic controller with change in machine resistance

Conclusion
Indirect Vector control of induction motor using conventional PI controller & Fuzzy logic controller is implemented using MATLAB/Simulink model. The induction machine model is created with the help of dq analysis.
This paper proposed an effective control strategy for Indirect vector control of Induction motor using Fuzzy Inference System (FIS). The steady state & dynamic performance of the induction motor drive can be considerably improved with the proposed strategy. The Fuzzy Inference System (FIS) is built with two inputs (error & change in error) & one output (command signal). This scheme doesnt require algorithms for estimation of rotor resistance when drive gets heated up which is the case with conventional PI control. The drive performance proves to be equally good even when the rotor resistance deviates from the actual value due to motor heating. The performance of the drive at different load torques & reference speeds with FIS controller against the conventional PI controller shows the effectiveness of the proposed scheme. The entire simulation study is performed using MATLAB/Simulink software using basic blocks.
The machine performance under parameter variation (in this case stator & rotor resistances due to heating of motor) using conventional PI controller & Fuzzy logic controller is shown with the help of plots. Performance of the machine under parameter variation is equally good with Fuzzy logic controller which failed to achieve the same performance with conventional PI controller. Fuzzy logic controller proved to be more effective & less sensitive to parameter variation.
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Venkat Ramana Reddy Anchuri is currently pursuing Masters in Power Electronics at Teegala Krishna Reddy Engineering College, Hyderabad. He completed his graduation in Electrical & Electronics
Engineering in the year 2007. His area of interest includes Power Electronic controllers, Machine Controls & Model Based Design.
Swarna Enugala is currently working as Assistant
Professor in CMR Engineering College, Hyderabad. She completed her graduation in Electrical & Electronics Engineering in the
year 2007 & Post Graduation in Power Electronics in the year 2013. Her areas of interest include Control Systems, Power Electronic Controllers & Power quality.
Raghavendar Inguva is currently working as an Associate professor in Teegala Krishna Reddy Engineering College, Hyderabad. He had completed his gradation in
the year 2004 and Post Graduation in 2006 in Electrical & Electronics Engineering. At present he is pursuing his research work in Osmania University on Dynamic State Estimation. He has published 5 research papers in International Journals. His area of interest is Power Systems Operation.