Online Tuning of Artificial Neural Networks for Induction Motor Control A THESIS Submitted by RAMA KRISHNA MAYIRI (M060156EE) In partial fulfillment of the requirements for the award of the Degree of MASTER OF TECHNOLOGY IN ELECTRICAL ENGINEERING (Computer Controlled Industrial Power) Under the Supervision of Dr. ABRAHAM T. MATHEW DEPARTMENT OF ELECTRICAL ENGINEERING NATIONAL INSTITUTE OF TECHNOLOGY NIT CAMPUS P.O, CALICUT-673601 KERALA, INDIA MAY 2008
ACKNOWLEDGEMENTS I express my profound sense of gratitude to my guide, Dr. Abraham T Mathew, Professor, Department of Electrical Engineering, NIT Calicut, for his systematic guidance and valuable advices. His encouragement and suggestions were of immense help to me throughout my project work. I would like to express my sincere gratitude to Dr. Paul. K. Joseph, Professor and Head, Department of Electrical Engineering, N.I.T. Calicut, for providing me with all the necessary facilities for the work. Also I thank Dr. K M Moideenkutty, Professor and Former Head, Department of Electrical Engineering, NIT Calicut. I would also like to thank all the faculty and staff members of EED, especially the staff of EED Simulation Lab and library, who extended full cooperation for completion of this work. I take the opportunity to thank all my friends who helped me through their patient discussions and suggestions and for their timely help at various stages during the tenure of my project work. Rama Krishna M M060156EE
DECLARATION "I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which has been accepted for the award of any other degree or diploma of any university or any institute of higher learning, except where due acknowledgment has been made by me in the text. Place: NIT Calicut Date: Name: Rama Krishna M Roll No: M060156EE
C E R T I F I C A T E This is to certify that the thesis entitled ONLINE TUNING OF ARTIFICIAL NEURAL NETWORKS FOR INDUCTION MOTOR CONTROL submitted by Mr. RAMA KRISHNA MAYIRI (M060156EE) to the National Institute of Technology Calicut towards the partial fulfillment of the requirements for the award of the Degree of Master of Technology in Electrical Engineering (Computer Controlled Industrial Power) is a bonafide record of the work carried out by him under my supervision. Dr. Abraham T Mathew (Project Guide) Professor Department of Electrical Engineering National Institute of Technology Calicut Place: NIT Calicut Date: Professor and Head Department of Electrical Engineering National Institute of Technology Calicut
CONTENTS Abstract List of symbols List of Figures List of Tables i ii iii v 1. Introduction 1.1. Introduction 1 1.2. Electric Motor Drives 2 1.3. Problem definition 3 1.4. Thesis Overview 5 2. Neural networks in Electrical Drives An Overview 2.1. Introduction 6 2.2. Control of Induction Motor using ANN 6 2.3. Conclusion 8 3. Experimental Determination of Machine parameters 3.1. Introduction 9 3.2. Measurement of Moment of Inertia 9 3.3. Induction Machine Parameters 10 3.4. Conclusion 15 4. Induction Motor Control using Online Tuning of NN 4.1. Introduction 16 4.2. Induction Machine Dynamics 16 4.3. Neural Network Identifier 20 4.4. Control of Induction Motor Stator Currents 22 4.5. Conclusion 25 5. Simulation Results 5.1. Introduction 26 5.2. Analysis on 5 hp Induction Motor 26 5.2.1. Induction motor Model 26
5.2.2. NN Speed Estimator 28 5.2.3. Comparison between NNs 32 5.2.4. Comparison between machine and NN 3 layer 34 5.2.5. Torque Comparison using NN Torque Estimator 36 5.2.5.1. Torque Estimator Using Speed 37 5.2.5.2. Torque Estimator Using Speed And Currents 39 5.3. Analysis on 7 hp Induction Motor 41 5.3.1. Induction Motor Model 41 5.3.2. NN speed Estimator 3 layer 42 5.3.3. NN speed Estimator 4 layer 47 5.3.4. NN Speed Estimator 4 layers with LEARNGDM 50 5.3.5. NN Speed Estimator 4 layers with different Goal 51 5.3.6. NN speed Estimator 5 layer 52 5.4. Induction motor stator currents control 53 5.4.1. Simulation model for fractional HP motor 55 5.4.2. Simulation model for 5 HP motor 57 5.5. Recurrent Neural Networks 58 5.6. Conclusion 63 6. Conclusion 64 References 65 Appendix A 68 Appendix B 71 Appendix C 73 Appendix D 74
ABSTRACT Due to the inherent features, such as ruggedness, high reliability, low cost, and minimum maintenance, the induction motors have been gradually replacing dc motors in servo applications and high performance drives. The wide use of induction motors in high precision drives, such as the robot manipulation, actuation calls for more advanced control techniques, so that their highly nonlinear dynamics can be handled properly and at a reasonable cost. Artificial Neural networks (ANNs) have learning, adaptation, and powerful nonlinear mapping capabilities. Therefore, they have been employed to deal with prediction, modeling, and control of complex, nonlinear, and uncertain systems, in which the conventional methods fail to give satisfactory results. In the recent years, some authors have proposed the use of artificial neural networks (ANNs) for identification and control of nonlinear dynamic systems in power electronics and ac drives. Taking clue from such works, Attempts have been made here to combine ANN with Induction Motor Drives to have a better closed loop feedback control of speed of the Induction Motors for some typical applications. Experiments were conducted on a Laboratory type Slip ring induction motors to obtain the machine parameters. Neural network Estimators have been developed using neural network toolbox of MATLAB, which uses the Training data obtained from experiments conducted on Induction motors. The Neural network estimates were tested and compared with experimental data. Validation of NN speed identifiers has been done using Simulink, which revealed that neural network was useful for predicting the Induction motor dynamics and hence could be used for the induction motor control. A neural-network-based identification and control scheme was subsequently tried. The given Artificial Neural Network was trained to capture the nonlinear dynamics of the motor. A control law was derived using the dynamics captured by the network, and same was employed to force the stator currents to follow prescribed trajectories. Indirect adaptive control has been used in this work. A lot of fine tuning work has to be made before the scheme is finally made workable. Realization of the control system using digital hardware will be a viable option for continuing the work proposed in the thesis i
LIST OF SYMBOLS J P Ia, Ib, Ic Moment of Inertia of Induction Machine Rated power of the Electrical Machine Three Phase Stator currents of Induction Motor T Discretisation time interval τ r Rotor time constant, τ s Stator time constant, σ R1 R2 L 11 L 22 L m T L Total leakage factor Stator resistance Rotor resistance Stator inductance Rotor inductances Mutual inductance, External load torque. λ α, λ β Rotor flux linkages in two phase reference frame i α i β Stator currents in two phase reference frame V α, V β Stator voltages in two phase reference frame ω Rotor angular velocity in rad/sec ii
LIST OF FIGURES Fig 3.1 Variation of stator currents with respect to speed of 5 hp motor 11 Fig 3.2 speed time characteristics of 5 hp induction motor 12 Fig 3.3 stator currents with respect to speed for 5 hp motor 13 Fig 3.4 Speed vs. time for 7 HP Induction Motor 14 Fig 4.1 feed forward neural network 21 Fig 4.2 Block diagram for the identification and control of the stator currents 23 Fig. 5.1 MATLAB SIMULINK induction motor Model 26 Fig.5.2 Induction Machine internal blocks 27 Fig 5.3 Torque 5 hp Induction motor 27 Fig 5.4speed of 5 hp Induction motor 28 Fig.5.5 Neural Network toolbox nntool for creating ANN 28 Fig 5.6 Training of Neural network with 2 layers 30 Fig 5.7 Neural network using two layers 31 Fig 5.8 Training of the neural network with 3 layers 33 Fig 5.9 Neural network with three layers 33 Fig 5.10 Simulink Model for Speed Comparison of Speed Estimator (3layer) And Motor 35 Fig 5.11 Comparison between 5 hp Motor speed and NN Speed Estimator 36 Fig. 5.12 Comparison of Torque Speed Characteristics b/n Motor and NN 38 Fig 5.13 Comparison of 5 hp Motor and Torque Estimator 40 Fig 5.14 Torque of 7 HP Induction Motor 41 Fig 5.15 Speed of 7 HP Induction Motor 42 Fig 5.16 NN Speed Estimator for 7 hp Motor with 3 layers 44 Fig 5.17 Comparison of Speed of 7 HP Motor and NN Speed Estimator With 3 layers 45 iii
Fig 5.18 Stator current Ia Vs Speed of Speed Estimator 3 layers and 7 HP Motor 45 Fig 5.19 Stator current Ib Vs Speed of Speed Estimator 3 layers and 7 HP Motor 46 Fig 5.20 Stator current IcVs Speed of Speed Estimator 3 layers and 7 HP Motor 46 Fig. 5.21 Neural network of speed Identifier of machine 2 with 4 layers 47 Fig 5.22 Speed Comparison using Simulink model between 7 HP Motor and NN 48 Fig 5.23 Stator current Ia Vs Speed of Speed Estimator 4 layers and 7 HP Motor 49 Fig 5.24 Stator current Ib Vs Speed of Speed Estimator 4 layers and 7 HP Motor 49 Fig 5.25 Stator current Ic Vs Speed of Speed Estimator 4 layers and 7 HP Motor 50 Fig 5.26 Speed Comparison b/w 7 HP Motor and NN 4 layer with Momentum 51 Fig 5.27 Speed Comparison between 7 HP Motor and NN 4 layer with.001 Goal 52 Fig 5.28 Speed Comparison between 7 HP Motor and NN 5 layer 53 Fig 5.29 feed forward neural network acts as Speed Identifier 54 Fig5.30 Closed loop control of Machine using ANN 55 Fig. 5.31 Fractional HP motor speed in rad/sec using closed loop strategy 56 Fig. 5.32 5 HP Induction motor speed in rad/sec using closed loop strategy 57 Fig 5.33 A simple Recurrent Neural Network 58 Fig 5.34 Elman Recurrent Neural Network Architecture 60 Fig 5.35 Block diagram of Recurrent NN for Induction Motor speed Estimation 63 iv
LIST OF TABLES Table 3.1 Observations of Retardation Test on 5 hp motor 11 Table 3.2 Retardation Test Observations of 7 HP Induction motor 13 Table 3.3 Parameters of 5 hp Induction Machine and 7 HP Induction Machine 15 Table 5.1 Experimental Data Currents vs. Speed 29 Table 5.2 NN Speed Estimator of 5 hp motor Testing and comparison 31 Table 5.3 Testing of ANN Speed Estimator and Comparison between Networks for 5 hp motor 34 Table5.4 Training Data for Torque Estimator using Speed for 5 hp motor Using Speed 37 Table5.5 Testing of NN Torque Estimator using Speed of 5 hp Induction Motor Using Speed 38 Table 5.6 Training Data for Torque Estimator for 5 hp motor Using Currents & Speed 39 Table 5.7 Testing of NN Torque Estimator using Currents & Speed 40 Table 5.8 Training Data for NN Speed Estimators for 7 hp Induction Motor 43 Table 5.9 Testing of NN Speed Estimator for 7 HP Induction Motor 44 Table 5.10 Testing of NN Speed Estimator of 7 HP Motor with 4 layer 48 v
CHAPTER 1 INTRODUCTION 1.1 INTRODUCTION In engineering and physics domains, algebraic and differential equations are used to describe the behavior and functioning properties of real systems and to create mathematical models to represent them. Such approaches require accurate knowledge of the system dynamics and the use of estimation techniques and numerical calculations to emulate the system operation. The complexity of the problem itself may introduce uncertainties, which can make the modeling nonrealistic or inaccurate. Therefore, in practice, approximate analysis is used and linearity assumptions are usually made. The majority of physical systems contain complex nonlinear relations, which are difficult to model with conventional techniques. Three Phase Induction motors, combined with the drive system can be deemed as a complex nonlinear system due to the various issues associated with its dynamics. Precise control of induction motor is a challenge and investigations in these directions are in progress in various parts of the world. It is proposed to use Artificial Neural Networks for the Control of Induction motors so that the on line tuning of the control can be incorporated in that system. Artificial neural networks (ANNs) implement algorithms that attempt to achieve a neurological related performance, such as learning from experience, making generalizations from similar situations and judging states where poor results were achieved in the past. ANN are being applied to a lot of real world, industrial problems, from functional prediction and system modeling (where physical processes are not well understood or are highly complex), to pattern recognition engines and robust classifiers, with the ability to generalize while making decisions about imprecise input data [1]. 1
The application of artificial neural networks (ANNs) attracts the attention of many scientists from all over the world. The reason for this trend is the many advantages, which the architectures of ANN have over traditional algorithmic methods. Among the advantages of ANN are the ease of training and generalization, simple architecture, possibility of approximating nonlinear functions, insensitivity to the distortion of the network, and inexact input data. The use of ANN is practical at present that technological progress is rapid and the practical utilization of the system that mimics nature is possible. ANNs can be used to identify and control nonlinear dynamic systems because they can approximate a wide range of nonlinear functions to any desired degree of accuracy. In case of electrical machines Speed can be identified using neural networks using the currents, voltage as inputs to the network. Torque Estimator can be developed using stator currents, speed and stator voltage. In some applications we can estimate the rotor flux and torque by giving stator currents of induction motor as inputs to neural networks. Artificial Neural networks can also used for Inverter control where NN estimates the Gate signals based on the error value between the actual stator currents and reference stator currents. A detailed review of related literature is given in Chapter 2. In the present work neural network has been used as speed Estimators. Neural network is used to estimate two reference frames of currents of induction motor to control the stator currents of induction motor. Speed, currents in previous instant control voltages etc are the input variables to the neural network. Only MATLAB based simulation has been done in the present work. A brief study of electrical drives is given below. 2