PSO Algorithm for Three Phase Induction Motor Drive ... - IEEE Xplore

2014 IEEE International Conference Power & Energy (PECON)

PSO Algorithm for Three Phase Induction Motor Drive with SVPWM Switching and V/f Control Jamal Ali Ali, M. A. Hannan, Azah Mohamed Dept. of Electrical, Electronic and System Engineering, Faculty of Engineering and Built Environments, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia [email protected], [email protected], [email protected] intelligent controller [1, 13]. Many optimization techniques have been used to improve the PI controller performances and to regulate the parameters of PI controller for example GA-PI optimization [12, 14]. The PSO algorithm is one method optimization techniques has the advantages of strong robustness and globe convergence capability, easy to implement [15]. In this paper, use PSO algorithm for tuning PI parameters found on best values (Kp and Ki) for becoming robustness speed controller on the TIM. To knowledge the robustness and performance of the novel response speed controller on the TIM of through two tests, changes are rated speed or mechanical load observed the speed controller has succeeded through the extracted results. The implement PSO algorithm by using (m-file) code MATLAB and design a speed control scheme using MATLAB/Simulink to implement a modified V/f controller on the TIM drive (5.4 hp) with the SVPWM technique.

Abstract— Optimization techniques are become more popular for the improvement in control of Three Phase Induction Motor (TIM). Also the Volt/Hz (V/f) control and space vector pulse width modulation (SVPWM) are used to reduce the harmonics level related to other control and modulation techniques. This paper deals with the tuning of PI controller parameters to be used in TIM. Particle Swarm Optimization (PSO) algorithm is used to tune each parameter of PI speed controller to improve the speed response performance of the TIM. By designing appropriate PSO algorithm, Kp and Ki of the PI speed controller parameters are tuned for TIM operation with SVPWM Switching and V/f Control. The performance of PI speed controller on the TIM is measured by estimating the change of speed and torque under speed response condition. It is found that the performance of the PI controller is robust in terms of overshoot, settling time, steady state error and RMSE. Keywords— three-phase induction machine; V/f controller; inverter; SVPWM; particle swarm optimization; (PSO-PI) controller.

II. MODEL OF TIM AND SCALAR CONTROL The present mathematical model of the TIM is analysis to some stationary equations. Then with the change of variables the complexity of these equations was converted from three poly phase to two phase winding (d-q) frame [16]. In other mean, the stator part and rotor part variables like voltage, current and flux linkages of a TIM are transferred to another reference model which remains stationary in the d-q axis reference frame is given as follows [17]. The flux linkages can be representing the equations following [18]:

I. INTRODUCTION TIMs are the most used electric drives in the industries and high performance variable speed drive application because its robustness, high efficiency reduced maintenance, high performance and low cost [1]. TIM is so widely used in industrial application, such as paper miles, robotics, steel miles, servos, transportation system, elevators, machines tools etc. [2]. There are many methods for speed control of TIMs fed through the Voltage Source Inverters (VSIs) using different pulse width modulation (PWM) techniques such as sinusoidal PWM (SPWM), hysteresis band (HB) and random pulse width modulation (RPWM), respectively [3-8]. The SVPWM is one of the best ways that have been used for PWM to reduce the harmonic quality and to extend the linear range of operation [911]. One method to control on TIM is conventional scalar control (V/f control), PI controller is used to control the speed response of the TIM drive. In general, the PI controllers have been widely used in control on many devices because their simple control structure, easy design and low cost [12]. It is advantage used for solves many problems such as high overshoot, high steady state error, oscillation of speed response and torque due to changes in mechanical load [1]. However, the disadvantage for PI controller is difficult the found on best values for parameters PI controller. The mean, the PI parameters (Kp and Ki) usually need manual retuning before being transferred to the process under control. The tuning methods for proportional-integral (PI) controllers are used the Ziegler–Nichols method and self-tuning fuzzy PI based

978-1-4799-7297-5/14/$31.00 ©2014 IEEE

1 1 1 1

1 2 3 4

Due to substituting the values of flux linkages as in (1) to (4), generated the following current equations are obtained as: 5

250


III.

6

SVPWM technique is special switching scheme of six power IGBTs of a 3-phase power converter. It is one best way the Pulse Width Modulation (PWM) due to reduce minimum harmonics relative to the other PWM techniques becoming best PWM techniques. Operation principle is rotation of this space vector can be executed by the variable frequency device to generate three phase sine waves to go TIM [22]. The first convert three phase ABC to the two dimensional plane using , Clark’s and Park’s Transformation, resulting in diagram showed in Fig. 1. The three-phase voltage source can be converted to a space vector using following equation [23].

7

8 Where ψ and ψ are the mutual flux linkages in the (dq) axis. The mutual flux equations are written as follows:

2 3

9

14

Where is a calculated time share for each sector as equations below [23]:

10 1

√3. T . |V | sin V 3

T

11

15

3

In the above equations, the speed ω is related to the torque by the following mechanical torque as: 2

SVPWM TECHNIQUE

12

T

√3. T . |V | V

sin 1

The TIM design and implement depended on the above equations and of build by using MATLAB/SIMLINK and execution by using parameters in the Table I. TABLE I. Parameters

f P J

3 T

TIM Parameters

Definition Power rated Voltage rated Speed rated Stator resistance Rotor resistance Stator reactance Rotor reactance Mutual reactance Frequency Number of poles Inertia of motor

Values 5.4 hp 400 V 1430 rpm 1.405 Ω 1.395 Ω 1.83437 Ω 1.83437 Ω 54.09822 Ω 50 Hz 4 0.01311 Kg.

T

16 17

T

where n=1 through 6 (that is sector 1 to 6) and 0 ⁄3, while T1 , T 2 and T o are the time vectors of respective voltage vectors, T z is the sampling period, and is the angle (call the theta) between the reference vector and the any space vector. The reference voltage Vref as in (15) and (16) is defined in terms of modulation index m [15]. 2

The main notion of scalar controller (V/f controller) for a TIM is control on the speed for a TIM at under change for rated speed or changes any mechanical load [19]. The best way for controlling a speed of a TIM is to good driving and also made a low cost and simple implement software and hardware [20]. The principle the work this controller where V and φ are the vectors of stator voltage and flux, and V and φ are their magnitude only, any change in the frequency effect magnitude voltage respectively. The equation below is explaining relationship between them [21]. 1 2

T

1 3

18

α

13

Fig. 1. Space vector diagram

251


IV.

DESIGN OF TUNING PSO BASED PI SPEED CONTROLLER

Primary PSO is an evolutionary computation technique developed by Eberhart and Kennedy in 1995, inspired by social behaviour of bird flocking, also represents one of the best ways of groups optimization techniques because strong robustness and globe convergence capability, easy to implement [12]. The particles in the PSO algorithm are searching the space in two locations. The first location is the best point in which the swarm has found in the current iteration (local best). The second location is the best point that found through all previous iterations (global best) [24, 25]. The operation Principle for PSO algorithm is depend on two factors velocity and position the member, represent update this factors by using equations below [15]. V t

1

wV t c r

X t

1

c r

P t

X t

P t

X t

X t

V t

1

Fig. 3. Flowchart of the PSO of the PI controller

19

In the Fig. 2, the Simulink model which represents the total structure for VSI fed to TIM. The modeling includes the speed controller called the PSO-PI speed controller is remove the error for control on the TIM through slip speed which add with rotor speed for new stator speed production and also the Simulink model generator includes the SVPWM technique that , as generator receives three voltages and convert to the switching time that goes to the IGBT (inverter) generator three phase voltage controller to operate the TIM.

20

Where, c1 is calling social rate and c2 is call cognitive rate. r1 and r2 is the random in the interval (0,1). V is the velocity factor of agent i at iteration d, t is the present iteration, w is the inertia factor. X is the position factor. This algorithm has been utilized to search on the best values for the pararmeters PI speed controller (Kp and Ki turning) for make robustness control on TIM as observes controller structure in the Fig. 2. Root Mean Square Error (RMSE) is chosen as the error performance to a satisfactory transient response to the speed TIM or call of objective function, which computes of the below [12].

RMSE

1 n

e

V. RESULT AND DISCUSSION The performance of the new PSO-PI speed controller on the TIM drive is a strong controller during the performance with different speed variations in the reference trajectory speed and mechanical load each after different intervals and it has been used in the PSO algorithm of (500) iteration to reach best results as show in the Fig. 4. The evaluation includes of the robustness controller performance for the control on the TIM with the sudden change in reference speed and sudden change in mechanical load. This model has been application by using MATLAB/SIMULINK and (m-file) code. The two stages will applied on the system for illustration robustness the design controller, the first test is variable load with speed constant and the second test is variable speed with load constant as show in the points below.

21

the error and n is no. of sample. Where e . Show in Fig. 3 the flow chart of the PSO algorithm of the PIspeed controller.

Fig. 2. Close loop diagram of the proposed speed controller of TIM

252


30

A. Variations load with speed constant

TABLE II.

M echanical load (N.m)

At this stage, working on the model operation by speed constant (full speed) with variation mechanical load in the more of stages. Show in Fig. 5.a the system speed response is applied variation mechanical load and Fig. 5.b show variation in mechanical load, in the starting the operating full speed (1430 rpm) but no load until 0.2sec, then carrying value quarter the mechanical load until 0.3sec, then carrying value half the mechanical load until 0.4sec, then carrying value three quarter the mechanical load until 0.5sec, then carrying value full the mechanical load until 0.6sec and return from full load to no load respectively. The disturbance will have there the response speed through the motor work with a change in motor mechanical load respectively as show in the Table II summarize their performance analysis. The good results on each change in the mechanical load as shown in the table.

Mech. load (N.m)

From

To

From

0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Starting 0 6.725 6.725 13.451 13.451 20.175 20.175 26.901 26.901 20.175 20.175 13.451 13.451 6.725 6.725 0

To

O. S. (%) 6 2.83 2.4 2.2 2.48 3.08 3.24 3.32 3.45

S. T. (sec) 0.1 0.03 0.035 0.025 0.02 0.027 0.03 0.025 0.03

20 15 10 5 0 0

0.1

VI.

RMSE (log)

-0.9602

-0.9605

10

-0.9608

Iteration

300

400

500

TABLE III.

Fig. 4. The relationship curve between RMSE and iteration

Speed (rpm)

1000

500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.7

0.8

0.9

1

CONCLUSIONS

PERFORMANCE OF SPEED RESPONSE WITH VARIATION LOAD

Time (sec)

1500

0 0

0.6

In the present work, a simulation model is developed for VSI-fed TIM drive using SVPWM technique and V/f control with the PSO-PI speed controller of the scalar control of the TIM. The effect of the change of the reference speed and mechanical load requirements have been discussed for the developed TIM operation. Moreover, the developed PSO-PI speed controller shows the best response to the TIM by reducing each of maximum overshoot, settling time, steady state error and RMSE. Therefore, it can be concluded that the robustness of the controller could be used in the TIM performance enhancement.

10

200

0.5

Time (sec)

To prove the good performance of the modified PSO-PI speed controller, a sudden change in reference speed at constant no-load is introduced. In the starting, the operating of full speed (1430rpm) until 0.3sec, then sudden change the speed to three quarter rated speed (1073rpm) until 0.4sec, then change to half rated speed (715rpm) until 0.5sec , then change to quarter rated speed (357rpm) until 0.6sec and also return up to full speed as show in the Fig. 6. Table III summarize the performance analysis for no-load with the sudden speed change is applied with the values.

S.S.E. (%) 0.4 0.2 0 0.2 0.3 0 0.1 0.2 0.3

10

100

0.4

B. Variations speed with load constant

-0.9599

0

0.3

Fig. 5. Speed response with change sudden of the load (b) auto mechanical load.

O.S.: overshoot; S.T.: settling time; S.S.E.: steady state error.

10

0.2

(b)

PERFORMANCE OF SPEED RESPONSE WITH VARIATION LOAD

Time (sec)

25

1

Time (sec)

(a)

253

Ref. speed (rpm)

From

To

From

To

0 0.3 0.4 0.5 0.6 0.7 0.8

0.3 0.4 0.5 0.6 0.7 0.8 1

0 1430 1073 715 358 715 1073

1430 1073 715 358 715 1073 1430

O.S. (%) 6 14 13 13 18 16 18

S.T. (sec) 0.1 0.025 0.03 0.32 0.028 0.033 0.04

S.S.E. (%) 0.4 0.38 0.35 0.3 0.35 0.4 0.3


Speed (rpm)

1500

[11]

1000

[12]

500

[13]

0 0

0.1

0.2

0.3

0.4

0.5

0.6

Time (sec)

0.7

act. speed ref. speed 0.8 0.9 1

[14]

Fig. 6. Speed response with change sudden of the reference speed

[15]

ACKNOWLEDGMENT The authors are grateful to Ministry of Science, Technology and Innovation, Malaysia for supporting this research financially under grant 06-01-02-SF1060.

[16]

REFERENCES [1]

[2]

[3]

[4]

[5] [6]

[7]

[17]

Arun Kumar R and Febin Daya J L, “A novel self - tuning fuzzy based pid controller for speed control of induction motor drive”, International Conference on Control Communication and Computing (ICCC), Thiruvananthapuram India, pp.62-66, Dec. 2013. Y. Zhang, Y. Zhang, J. Wang and R. Ma, “An active disturbance rejection control of induction motor using DSP+FPGA”, Control and Decision Conference (CCDC), Guiyang Chine, pp.4047 - 4052, May 2013. K. Kumar and K. Sivakumar “A Quad Two-Level Inverter Configuration for Four Pole Induction Motor Drive with Single DC Link”, IEEE Trans. Ind. Electron., pp.1-8, 2013. M. F. bin Romlie, M. Fadhil Pesol and K. Nisak Md Hasan“PWM Technique to Control Speed of Induction Motor using Matlab/xPC Target Box” IEEE International Conference on Power and Energy (PECon 08), Johor Baharu, Malaysia, pp. 718-721, Dec. 2008. K. A. Corzine “A Hysteresis Current-Regulated Control for Multi-Level Drives” IEEE Trans. Energy Convers., VOL. 15, pp. 169-175, 2000. K. A. Almarri, J. C. Balda and K. Can “Optimized Selection of the Random PWM Switching Frequencies in a Limited Pool” Applied Power Electronics Conference and Exposition, New Orleans, LA, pp. 569-573, 2000. C. B. Jacobina, A. M. N. Lima, E. R. C. Silva and A. M. Trzynadlowski “Current Control for Induction Motor Drives Using Random PWM”

[18]

[19]

[20]

[21]

[22]

[23]

IEEE Trans. Ind. Electron., Vol. 45, pp. 704-712, 1998.

[8]

M. V. Rajkumar and P.S. Manoharan, “FPGA based multilevel cascaded inverters with SVPWM algorithm for photovoltaic system”, Sol. Energy. Vol.87, pp.229–245, 2013. [9] M. V. Rajkumar, P.S. Manoharan and A. Ravi, “Simulation and an experimental investigation of SVPWM technique on a multilevel voltage source inverter for photovoltaic systems”, Electr. Power Syst. Res., Vol.52, pp.116–131, 2013. [10] M. H. V. Reddy and V.Jegathesan “Open loop V/f Control of Induction Motor based on hybrid PWM With reduced torque ripple” Emerging

[24]

[25]

254

Trends in Electrical and Computer Technology (ICETECT), Tamil Nadu India, pp. 331-336, Mar. 2011 N. V. Naik and S. P. Singh “A Novel Type-2 Fuzzy Logic Control of Induction Motor Drive Using Scalar Control” Power Electronics (IICPE), Delhi India, pp.1-6, Dec. 2012. X. Fang, W. Qun-jing and L. Guo-li, “Optimization research of FOC based on PSO of induction motors”, Electrical Machines and Systems (ICEMS), Sapporo Chine, pp.1-4, Oct. 2012. J. C. Basilio and S. R. Matos “Design of PI and PID Controllers With Transient Performance Specification” IEEE Trans. Educ., VOL. 45, pp. 364-370, 2002. T. Jin, P. Li, L. Zhao, X. Du and X. Ma “Optimization of Hydrodynamic and Hydrostatic Steering Control System Based on GA-PID” Control and Decision Conference (CCDC), Xuzhou China, pp. 3180-3184, May 2010. M. N. Afrozi, M. H. Aghdam, A. Naebi and S. H. Aghdam “Simulation and optimization of asynchronous AC motor control by Partial Swarm Optimization (PSO) and emperor algorithm”, Computer Modeling and Simulation (EMS), Madrid Spain, pp.251-256, Nov. 2011. M Bendjedia, K A Tehrani and Y Azzouz, “Design of RST and fractional order PID controllers for an induction motor drive for electric vehicle application”, Power Electronics, Machines and Drives (PEMD), Manchester United Kingdom, pp.1-8, April 2014. Y. Yang, Q. Wu and S. Ding, “BP neural network based PID controller for AC servo drive system”, Intelligent Control and Information Processing (ICICIP), Harbin Chine, pp.1079-1081, July 2011. P. M. Menghal and A. J. Laxmi, “Adaptive neuro fuzzy based dynamic simulation of induction motor drives”, International Conference on Fuzzy Systems (FUZZ), Hyderabad India, pp.1-8, July 2013. A. Buasrea and W. Subsinghaa, “Double-sided linear induction motor control using space vector pulse width modulation technique”, Energy Procedia, Vol.34, pp. 181-188, 2013. A. S. Abdel-Khalik and S. M. Gadoue, “Improved flux pattern by third harmonic injectionfor multiphase induction machines using neural network”, Alexandria Engineering Journal, Vol.50, pp.163-169, 2011. A. Idir and M. Kidouche, “Real-time simulation of V/f scalar controlled induction motor using RT-lab platform for educational purpose”, International Conference on Systems, Control and Informatics, Boumerdes Algeria, pp.189-192, 2013. M. Naim, B. Singh, S. P. Singh, R. Mishra, P. N. Verma, D. Kumar, and R. P. Vishvakarma, “Investigation of transient performance of VSI-fed IM drives with SVPWM technique based on P, PI and PID controllers”, Power, Control and Embedded Systems (ICPCES) , Allahabad India, pp.1-7, Dec. 2012. Honório, D.A., Diniz, E.C, Júnior, A.B.S., Almeida, O. M and Barreto, L.H.S.C, “Comparison between sliding model control and vector control for a DSP-based position control applied to squirrel-cage induction motor”, International Conference on Industry Applications (INDUSCON), Sao Paulo Brazil, pp.1-6, Nov. 2010. E.H.E. Bayoumi, M. Awadallah, and H. Soliman,” Deadbeat performance of vector-controlled induction motor drives using particle swarm optimization and adaptive neuro-fuzzy inference systems ”. Electromotion Scientific Journal, Vol.18, pp. 231-242, 2011. M.Awadallah, E.H.E.Bayoumi and H.M.Soliman, “Adaptive Deadbeat Controllers for BLDC Drives using PSO and ANFIS Techniques”, Journal of Electrical Engineering, Vol 60, pp. 3-11, 2009.