Ann Based Soft Starting Of Voltage Controlled Fed Im Drive System

IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005

497

ANN-Based Soft Starting of Voltage-Controlled-Fed IM Drive System Adel Gastli, Senior Member, IEEE, and Mohamed Magdy Ahmed, Member, IEEE

Abstract—Soft starters are used as induction motor controllers in compressors, blowers, fans, pumps, mixers, crushers and grinders, and many other applications. Soft starters use ac voltage controllers to start the induction motor and to adjust its speed. This paper presents a novel artifical neural network (ANN)-based ac voltage controller which generates the appropriate thyristors’ firing angle for any given operating torque and speed of the motor and the load. An ANN model was designed for that purpose. The results obtained are very satisfactory and promising. The advantage of such a controller are its simplicity, stability, and high accuracy compared to conventional mathematical calculation of the firing angle which is a very complex and time consuming task especially in online control applications. Index Terms—AC voltage controller, artificial neural network (ANN), firing angle, induction motor, soft starter, thyristor.

I. INTRODUCTION

L

IKE induction motor (IM) variable speed drives, soft starters are also essential components in every modern IM drives and automation systems. Ac voltage-controller-based soft starters offer many advantages over conventional starters such as the following. • Smooth acceleration, which reduces stress on the mechanical drive system due to high starting torque hence increases the life and reliability of belts, gear boxes, chain drives, motor bearings, and shafts [1]. Smooth acceleration reduces also stress on the electrical supply due to high starting currents meeting utility requirements for reduced voltage starting and eliminating voltage dip and brown out conditions[2]–[4]. It reduces also the shock on the driven load due to high starting torque [4], [6] that can cause a jolt on the conveyor that damages products, or pump cavitations and water hammer in pipes. Thus, a fully adjustable acceleration (ramp time) and starting torque for optimal starting performance, provides enough torque to accelerate the load while minimizing both mechanical and electrical shock to the system [6]. • Energy savings at lightly loaded conditions. Energy savings by voltage control is achieved by reducing the applied voltage if the load torque requirement can be met with less than rated flux. This way, core loss and stator copper losses can be reduced [7], [8].

Manuscript received June 15, 2001; revised April 7, 2004. Paper no. 2001TR328. The authors are with the Department of Electrical and Computer Engineering, Sultan Qaboos University, Al-Khodh, Muscat 123, Sultanate of Oman (e-mail: [email protected]; [email protected]). Digital Object Identifier 10.1109/TEC.2004.841522

Fig. 1.

Symmetrical ac voltage controller.

Soft starters allow the machine to start, vary its speed, and stop with minimum mechanical and electric stresses on the equipment. This can be done by appropriate adjustment of the IM terminal voltage. However, adjusting the voltage for a given operating condition of speed and torque is not a very simple task. To adjust the voltage, the firing angle of the thyristors shall be calculated for each operating condition. This firing angle is a nonlinear function of the motor speed and torque and it is quite difficult to find the exact value of for any motor speed and torque. Some methods of closed loop control of ac voltage regulators have been developed and applied, which requires a speed sensor [9]. In [6] and [10], the authors have proposed a method of optimal soft starting without a speed sensor but it requires sensing of the thyristors voltages. This paper proposes an artifical neural network (ANN)-based selection of the thyristors firing angles of a voltage-controlled-fed IM drive system. The controller operates in open loop and does not require any speed or voltage sensing. The only sensor that is needed is a current sensor, which in most of applications is used to protect the converter and the motor from over currents. The soft starter is designed to meet the industrial requirements of compressors, blowers, fans, pumps, mixers, crushers and grinders, etc. II. SOFT STARTER A soft starter is an ac voltage controller in which the voltage is adjusted through the setting of the thyristors firing angle . Fig. 1 shows a typical configuration of a symmetrical voltage controller. The six thyristors in Fig. 1 are fired according to the sequence shown in Fig. 2. Note that at least two thyristors must conduct simultaneously to allow current to flow through the load and that the firing angle is measured from the zero crossing of phase A voltage.

0885-8969/$20.00 © 2005 IEEE

498

IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005

Fig. 4. Torque versus speed characteristics for different values of the thyristors firing angle. Fig. 2. Thyristors firing logic.

Fig. 3. Matlab/Simulink simulation model used to generate data for ANN training.

A soft-starter-fed three-phase induction motor was modeled and simulated using Matlab/Simulink Power system blocksets as shown in Fig. 3. The asynchronous motor and all power electronics switches were modeled according to their operating characteristics. The Simulink model was used for predicting the performance of the soft starter and has given satisfactory results. The program is run several times with a fixed firing angle and varying load torque. The steady state value of the speed is calculated after each run. The same procedure is repeated for different values of the firing angle. The system parameters used in the simulation are very similar to those of actual lab equipment: 1/3 hp, 4 poles, 50-Hz, 220-V, star-connected, squirrel-cage induction motor. The per-unit motor parameters obtained using the rated voltage as a base voltage and 375 W as a base power are

Fig. 5.

Block diagram of ANN model.

of the firing angle smaller or equal to the load impedance angle , there is a continuous conduction. Thus, changing the firing angle within that limit will have no effect on the voltage applied to the motor; hence, the torque and speed values are kept unchanged. III. ANN The ANN model, used for the calculation of the appropriate thyristors’ firing angle as a function of the motor speed and torque , has two input variables ( and ) and one . Since the angle is a nonlinear function of output variable the and , then the tansigmoidal function given by (1) is the most appropriate to model it [11] (1)

Fig. 4 shows the torque-versus-speed characteristics obtained for different values of thyristors’ firing angle. Note that for small values of , from 0 up to 30 the speed torque characteristics are very similar. This can be explained by the fact that for values

The typical two-layer architecture used for the firing angle calculation is shown in Fig. 5. It has a hidden layer of tansigmoidal neurons, which receive inputs (in this case the speed and the torque) directly, then broadcast their outputs to a layer of linear neurons, which compute the network output (in this case the firing angle).

GASTLI AND AHMED: ANN-BASED SOFT STARTING OF VOLTAGE-CONTROLLED-FED IM DRIVE SYSTEM

TABLE I PARAMETERS OF THE NEURAL NETWORK MODEL

499

newly generated sets of speed and torque patterns are input to the Matlab neural net model and the corresponding pattern of firing angle is calculated systematically. According to Fig. 6, it is noticed that there is a very good fitting between both ANN and actual results, which proves that the designed ANN model is very precise. It is important to note that this accuracy is valid only for the input data that is within the boundaries specified during the training of the ANN model (see Table I for maximum and minimum input values). Beyond those boundaries, the accuracy may not be as good as expected. IV. SIMULATION A. Procedure The ANN model and the drive system were implemented with Simulink Power System Blockset and Simulink Neural Network Toolbox as shown in Fig. 7. The weight and biases used are those obtained from the previously trained network in Table I. The load was considered as a pump or a fan with the following torque-speed characteristic: (2)

Fig. 6.

Comparison between actual and ANN results.

This architecture has been proven capable of approximating any function with finite number of discontinuity with arbitrary accuracy [11]. If an input set of data corresponds to a definite speed and torque pattern, the network can be trained to give a correspondingly desired firing angle pattern at the output. The network has the capability to learn from a sample set of input output data within a certain boundaries. The back-propagation-training algorithm is most commonly used for this type of feed-forward neural networks. The training is automated with Matlab simulation program that uses a certain number of input–output sample patterns. The sample patterns can be derived by experiments or by simulations. In our case, the patterns were derived by simulation using the computer program mentioned in Section II (Fig. 3). Part of the generated data is used to train the neural network. At the end of the training process (when the target mean squared error is reached), the model obtained consists of the weight and the bias vectors. Table I summarizes the parameters of the ANN model and the results of the training. The obtained weight and bias vectors are saved in a file that will be used during the simulation of the IM drive system. To check the accuracy of the designed ANN model, the actual values of the firing angle obtained by simulation were compared to those obtained by the ANN (see Fig. 6). Note that the samples used for this comparison are different from the samples used for the training of the ANN model. The

is a constant. where . Based The input to the program is the reference speed on the setting of the initial conditions, the program can simulate any operating condition of the system such as starting, breaking, or speed control. The reference torque is calculated based on the reference speed and load characteristic (2). Both the reference speed and torque are input to the ANN model, which produces the corresponding firing angle. A triggering logic module generates pulses to the six thyristors according to the firing pattern in Fig. 2. The outputs of the program are: • instantaneous values of the line currents and voltages; • instantaneous values of the motor torque and speed; • rms value of the current and average value of the torque. B. Results The system was first tested during soft starting operation following a linear speed ramp increase of 0.08 p.u./s. The reason behind using ramp increase and not step increase of the speed is because, as it was mentioned before, one of the advantages of using soft starters is to produce less electrical and mechanical stresses on the drive system during motor starting and speed variations. The simulation results are presented in Fig. 8. It is clear that, at steady state, the speed reaches the same value as the reference speed. To illustrate the benefits of using the ANN-based soft starter, direct-on-line motor starting simulations were also carried out. The results are shown in Fig. 9. Notice that, in the case of di(Fig. 9), the rms current and avrect-on-line starting erage torque oscillate and reach values higher than three times their rated values before reaching their steady-state values. This sudden increase and oscillations of the current put stresses on the electrical supply and create voltage dips. The oscillations of the torque decrease the life and reliability of belts, gearboxes,

500

Fig. 7.

IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005

Matlab/Simulink simulation model with ANN.

Fig. 9.

Simulation results of direct-on-line motor starting.

Thus, it provides enough torque to accelerate the load while minimizing both mechanical and electrical shock to the system. The designed ANN-based control of the soft starter was also tested for speed control. Fig. 10 shows the simulation results when the speed varied linearly from 0.5 to 0.8 p.u., then back to 0.5 p.u. The reference speed was varied following a ramp of 0.1 p.u./s. Notice that the actual values of speed and torque follow very closely the reference values. Moreover, the current and torque variations are taking place very smoothly, thus, avoiding high electrical and mechanical stresses on the system. V. EXPERIMENTS

Fig. 8. Simulation results of motor soft starting characteristics: (a) torque [p.u.], (b) speed [p.u.], (c) firing angle, and (d) current rms [p.u.].

chain drives, motor bearings and motor shafts. However, in the case of soft starting (Fig. 8), the oscillations vanish and the current and torque vary smoothly toward their steady state values.

The proposed control algorithm was tested by experiments using the hardware configuration shown in Figs. 11 and 12. The experimental setup uses the LabVolt 0.2-kW electromechanical system (model 8006) that is controlled with a PC through a digital-to-analog converter. The system consists of the following hardware equipment. • 1/3 hp, 4 pole, 50-Hz, 220-V, star-connected, squirrel-cage induction motor. • DC dynamometer as a load.

GASTLI AND AHMED: ANN-BASED SOFT STARTING OF VOLTAGE-CONTROLLED-FED IM DRIVE SYSTEM

501

Fig. 11. Photograph of the experimental system using the LabVolt 0.2-kW electromechanical system, model 8006.

Fig. 10. Simulation results for speed control: (a) torque [p.u.], (b) speed [p.u.], (c) firing angle, and (d) current rms [p.u.].

• Three-phase 220-V, 50-Hz balanced power supply. • AC regulator unit using six power thyristors. • Thyristor firing unit controlled by a dc voltage (Volts Firing angle). • Digital-to-analog converter which converts a digital voltage value output from the PC parallel port into an analog voltage that is fed to the thyristor firing unit. • PC unit which calculates the appropriate firing angle for each given speed and torque using the proposed control algorithm. The output data through the parallel port is the equivalent dc voltage that is required by the thyristor firing unit to generate the appropriate pulses at the required firing angle. The experiments were conducted in order to check if the reference speed of the motor set by the PC program is the same as the one measured at the motor shaft for different load torque values. These load torques were 0.3, 0.45, and 0.6 Nm. The experiments were conducted following the procedure described hereafter: • The load torque of the motor is set to 0.3 Nm on the dynamometer side.

Fig. 12.

Schematics of the experimental system configuration.

• The designed PC program is run which asks to enter from the keyboard the values of torque and speed. • The entered torque value is 0.3 and the speed value is entered according to the values used to train the ANN model. • The program calculates the corresponding firing angle based on the ANN model and converts it to an equivalent binary dc voltage value. • This binary dc voltage value is output to the parallel port and converted to an analog value by the digital-to-analog converter.

502

IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005

Note also that these results validate the good accuracy of the Matlab/Simulink simulation model since the ANN model was built based on training data obtained from the simulation model and not from the experiments. VI. CONCLUSIONS

Fig. 13.

Reference and actual speed at T = 0:3 Nm.

In this paper, a novel method for controlling soft-starter-fed induction-motor-drive systems using ANN is introduced. The method consists of training a two-layer ANN model on a set of data generated by simulation or experiments. The generated data are the speed and torque patterns as inputs and their corresponding firing angle patterns as output of the ANN model. The ANN model was trained successfully and the results of comparison between the actual data and the ANN calculated data were very satisfactory. To validate the effectiveness of the proposed soft starter control scheme, an induction motor fan drive system, fed by the proposed soft starter, was implemented by software program and hardware experimental setup. Several simulations and experiments were carried out for different operating conditions and the results were very satisfactory. Thus, the ANN approach has resolved the problem of the complexity of the online determination of the appropriate thyristor firing angle for any operating condition. It is also important to note that the controller operates in open loop which has the advantage of being stable and does not require any speed, or voltage sensing. The proposed soft starter is designed to meet the industrial requirements of compressors, blowers, fans, pumps, mixers, crushers and grinders, etc. REFERENCES

Fig. 14.

Speed error versus actual speed for different load torque values.

• Finally, this dc voltage is converted to an equivalent thyristor firing signal that controls the thyristors with the selected angle which in turns set the motor voltage to the desired value. • The same procedure is repeated for different speed and torque values. Fig. 13 shows the experimental results obtained for the three sets of load torque values: 0.3, 0.45, and 0.6 Nm (high, medium, and light load). Comparing the actual speed (diamond points) to the reference speed (solid line), one can clearly notice the good fitting between these values. Fig. 14 shows the absolute error between the reference speed (the value entered to the program) and the actual speed (the reading in the dynamometer screen) as a function of the reference speed at different load torques. It is clear from the results shown in Fig. 14 that the maximum speed error is below 0.4%, which is relatively very small. This result validates the previous statement that the proposed control algorithm is very accurate.

[1] R. F. McElveen and M. K. Toney, “Starting high-inertia loads,” IEEE Trans. Ind. Appl., vol. 37, no. 1, pp. 137–144, Jan./Feb. 2001. [2] A. J. William and M. S. Griffith, “Evaluating the effects of motor starting on industrial and commercial power systems,” IEEE Trans. Ind. Appl., vol. IA-14, no. 4, pp. 292–299, Jul./Aug. 1978. [3] F. M. Bruce, R. J. Craefe, A. Lutz, and M. D. Panlener, “Reduced-voltage starting of squirrel-cage induction motors,” IEEE Trans. Ind. Appl., vol. IA-20, no. 1, pp. 46–55, Jan./Feb. 1984. [4] J. Nevelsteen and H. Aragon, “Starting of large motors—Methods and economics,” IEEE Trans. Ind. Appl., vol. 25, no. 6, pp. 1012–1018, Nov./Dec. 1989. [5] A. A. Shaltoutn, “Analysis of tortional torques in starting of large squirrel cage induction motors,” IEEE Trans. Energy Convers., vol. 9, no. 1, pp. 135–141, Mar. 1994. [6] G. Zenginobuz, I. Çadirci, M. Ermis, and C. Barlak, “Soft starting of large induction motors at constant current with minimized starting torque pulsations,” IEEE Trans. Ind. Appl., vol. 37, no. 5, pp. 137–144, Sept./Oct. 2001. [7] F. Blaabjerg, J. K. Pedersen, S. Rise, H. H. Hassen, and A. M. Trzynadlowski, “Can soft-starters help save energy?,” IEEE Ind. Appl. Mag., vol. 3, no. 5, pp. 56–66, Sep./Oct. 1997. [8] N. Mohan, “Improvement in energy efficiency of induction motors by means of voltage control,” IEEE Trans. Power App. Syst., vol. PAS-99, pp. 1466–1471, Jul./Aug. 1980. [9] S. B. Dewan, G. R. Slemon, and A. Straughen, Power Semiconductor Drives. New York: Wiley Interscience, 1984. [10] V. V. Sastry, M. R. Prasad, and T. V. Sivakumar, “Optimal soft strating of voltage-controller-fed IM drive based on voltage across thyristors,” IEEE Trans. Power Electron., vol. 12, no. 6, pp. 1041–1059, Nov. 1997. [11] D. Howard and B. Mark, “Neural network toolbox for use with matlab,” in User Guide: The Math Works Inc., 1992.

GASTLI AND AHMED: ANN-BASED SOFT STARTING OF VOLTAGE-CONTROLLED-FED IM DRIVE SYSTEM

[12] T. A. Lipo, “The analysis of induction motor with voltage controlled by symmetrically triggered thyristors,” IEEE Trans. Power App. Syst., vol. PAS-90, no. , pp. 515–525, Mar. 1971.

Adel Gastli (S’89–M’93–SM’00) received the B.Sc. degree in electrical engineering from Ecole Nationale des Ingénieurs de Tunis, Tunisia, in 1985, and the M.Sc. and Ph.D. degrees from Nagoya Institute of Technology, Nagoya, Japan, in 1990 and 1993, respectively. He was with the R&D Department at Inazawa Works (elevators and escalators) of Mitsubishi Electric Corporation, Japan, from April 1993 to August 1995. He is currently an Associate Professor of Electrical Engineering at Sultan Qaboos University, Muscat, Oman. His current research interests include electrical machines, power electronics, drives, and control.

503

Mohamed Magdy Ahmed (M’85) received the B.Sc. and M.Sc. degrees in electrical engineering from Alexandria University, Alexandria, Egypt, in 1974 and 1980, respectively, and the Ph.D. degree from UMIST, Manchester, U.K., in 1985. He is currently an Assistant Professor of Electrical Engineering at Sultan Qaboos University, Muscat, Oman. His research interests are in the area of simulation and electric drives. Dr. Ahmed is a member of the IEEE Industry Applications, Power Electronics, and Industrial Electronics societies.