OFF-LINE AND ON-LINE PARAMETER ESTIMATION OF INDUCTION MACHINES DUY CHAU HUYNH Thesis submitted for the Degree of Doctor of Philosophy Heriot-Watt University Department of Electrical, Electronic and Computer Engineering October 2010 The copyright in this thesis is owned by the author. Any quotation from the thesis or use of any of the information contained in it must acknowledge this thesis as the source of the quotation or information. ABSTRACT This thesis addresses off-line and on-line parameter estimations of an induction machine (IM) which are necessary to improve its control and operational performances. For off- line parameter estimation, two advanced particle swarm optimization (PSO) algorithms, known as the dynamic PSO and chaos PSO algorithms, are proposed for off-line parameter estimation of the three-phase and single-phase IMs.
The experimental results obtained compare the estimated parameters with the IM parameters achieved using the DC, no-load and locked-rotor tests for the three-phase IM and the load tests for the single-phase IM. There is also a comparison of the solution quality between a genetic algorithm (GA), standard PSO, dynamic PSO and chaos PSO algorithms. Additionally, a recursive least-squares (RLS) algorithm with multiple time-varying forgetting factors is proposed for on-line parameter estimation of the IM which can efficiently track the IM parameter variations during operation. Simulation results of the on-line estimated IM parameters using the proposed RLS algorithm are compared with the IM parameters obtained using other RLS algorithm variants.
Energy efficient control of the IM is also an important topic examined in this thesis. A control strategy is proposed using an optimal IM rotor flux reference. Two techniques, known as the derivative technique and the chaos PSO algorithm are proposed for obtaining the optimal IM rotor flux reference. Furthermore, the on-line parameter estimator using the RLS algorithm with multiple time-varying forgetting factors is used in this application to update the IM parameter variations so that the optimal IM rotor flux reference is always accurate and the IM efficiency always remains optimal.
Simulations are implemented to confirm the effectiveness of the proposed strategy for energy efficient control of the IM. i ACKNOWLEDGEMENTS Firstly, I would like to express my deepest gratitude to my supervisor, Dr. Dunnigan for his enthusiasm, patience, guidance and support from the initial to the final level of the research and the writing of this thesis. Acknowledgement is also given to other members of the Power Electronics and Drives Laboratory, technicians of the Workshop and staff members in the Electrical, Electronic and Computer Engineering Department for their assistance and technical supports.
I also would like to thank the Committee on Overseas Training Projects – Project No. 322, Ministry of Education and Training, Government of Vietnam that supported the finance for me. Finally, I would also like to thank my dad, mum, all members of my family and my friends for their support and encouragement throughout the years of my PhD study. ii ACADEMIC REGISTRY Research Thesis Submission Name: DUY CHAU HUYNH School/PGI: School of Engineering and Physical Sciences Version: (i.
First, Final Degree Sought Doctor of Philosophy Resubmission, Final) (Award and Electrical Engineering Subject area) Declaration In accordance with the appropriate regulations I hereby submit my thesis and I declare that: 1) the thesis embodies the results of my own work and has been composed by myself 2) where appropriate, I have made acknowledgement of the work of others and have made reference to work carried out in collaboration with other persons 3) the thesis is the correct version of the thesis for submission and is the same version as any electronic versions submitted*. 4) my thesis for the award referred to, deposited in the Heriot-Watt University Library, should be made available for loan or photocopying and be available via the Institutional Repository, subject to such conditions as the Librarian may require 5) I understand that as a student of the University I am required to abide by the Regulations of the University and to conform to its discipline. * Please note that it is the responsibility of the candidate to ensure that the correct version of the thesis is submitted. Signature of Date: Candidate: Submission Submitted By (name in capitals): DUY CHAU HUYNH Signature of Individual Submitting: Date Submitted: For Completion in Academic Registry Received in the Academic Registry by (name in capitals): Method of Submission (Handed in to Academic Registry; posted through internal/external mail): E-thesis Submitted (mandatory for final theses from January 2009) Signature: Date: Please note this form should bound into the submitted thesis.
Updated February 2008, November 2008, February 2009 TABLE OF CONTENTS Abstract i Acknowledgements ii Table of contents iv List of symbols ix Chapter 1 Introduction 1.2 Off-line parameter estimation of a three-phase induction machine 2 1.3 On-line parameter estimation of a three-phase induction machine 2 1.4 Energy efficient control of a three-phase induction machine 3 1.5 Off-line parameter estimation of a single-phase induction machine 3 1.6 Structure of the thesis 4 1.7 References 6 Chapter 2 Background theory and literature review 2.2 Modelling techniques of a three-phase induction machine 8 2.1 Per-phase equivalent circuit model 9 2.3 Field-oriented control of a three-phase induction machine 14 2.4 Off-line parameter estimation approaches for a three-phase 19 induction machine 2.1 DC, no-load and locked-rotor tests 19 2.2 No-load test 20 2.3 Locked-rotor test 22 2.2 Particle swarm optimization algorithms 28 2.3 Other optimization algorithms 29 2.5 On-line parameter estimation approaches for a three-phase 30 induction machine iv 2.1 Model reference adaptive systems 31 2.3 Recursive least-squares algorithms 32 2.6 Energy efficient control strategies for a three-phase induction machine 35 2.1 Model-based control 35 2.2 Genetic algorithms and particle swarm 37 optimization algorithms 2.7 Off-line parameter estimation approaches of a single-phase 42 induction machine 2.8 References 42 Chapter 3 Background to and modifications of a particle swarm optimization algorithm 3.2 Standard particle swarm optimization algorithm 55 3.3 Particle swarm optimization algorithm modifications 59 3.1 Particle swarm optimization algorithm with a constriction factor 60 3.2 Particle swarm optimization algorithm with a time-varying 61 inertia weight 3.3 Dynamic particle swarm optimization algorithm 63 3.4 Chaos particle swarm optimization algorithm 66 3.4 Comparison of the particle swarm optimization algorithm 70 with other evolutionary computation techniques 3.6 References 71 v Chapter 4 Off-line parameter estimation of a three-phase induction machine using particle swarm optimization algorithms 4.2 Off-line parameter estimation 75 4.1 Induction machine model for off-line parameter estimation 75 4.3 Off-line parameter estimation using particle swarm optimization algorithms 79 and a genetic algorithm 4.1 Standard particle swarm optimization algorithm 79 4.2 Dynamic particle swarm optimization algorithm 82 4.3 Chaos particle swarm optimization algorithm 85 4.7 References 99 Chapter 5 On-line parameter estimation of a three-phase induction machine using recursive least-squares algorithms 5.2 Induction machine model for on-line parameter estimation 106 5.3 On-line parameter estimation using recursive least-squares algorithms 109 5.1 Standard recursive least-squares algorithm 109 5.2 Recursive least-squares algorithm with a constant forgetting factor 112 5.3 Recursive least-squares algorithm with a time-varying 114 forgetting factor 5.4 Recursive least-squares algorithm with multiple forgetting factors 118 5.5 Recursive least-squares algorithm with multiple time-varying 123 forgetting factors 5.1 Case 1 – Constant induction machine parameters 127 5.2 Case 2 – Time-varying induction machine parameters 130 with the same variation rate 5.3 Case 3 – Time-varying induction machine parameters 135 with different variation rates vi 5.6 References 140 Chapter 6 Energy efficient control of a three-phase induction machine 6.2 Energy efficient control 145 6.1 Induction machine model for energy efficient control 145 6.2 Energy efficient control using an optimal rotor flux reference 147 6.3 Energy efficient control techniques 148 6.2 Chaos particle swarm optimization algorithm 149 6.4 Energy efficient control with on-line parameter estimation 152 6.1 Energy efficient control using the derivative technique 153 6.2 Energy efficient control using the chaos particle swarm 156 optimization algorithm 6.3 Energy efficient control with on-line parameter estimation 158 6.7 References 162 Chapter 7 Off-line parameter estimation of a single-phase induction machine using particle swarm optimization algorithms 7.2 Off-line parameter estimation 168 7.1 Single-phase induction machine model 168 7.2 Single-phase induction machine model with the core loss effect 170 7.3 Single-phase induction machine model with the rotor deep bar effect 171 7.3 Off-line parameter estimation using particle swarm optimization algorithms 173 7.1 Standard particle swarm optimization algorithm 174 7.2 Dynamic particle swarm optimization algorithm 178 7.3 Chaos particle swarm optimization algorithm 181 7.6 References 193 vii Chapter 8 Conclusions and author’s contribution 8.
