Multidisciplinary Design Optimization Methods for Electrical Machines and Drive Systems (eBook)

Gang Lei received the B.S. degree in Mathematics from Huanggang Normal University, China, in 2003, the M.S. degree in Mathematics and Ph.D. degree in Electrical Engineering from Huazhong University of Science and Technology, China, in 2006 and 2009, respectively. He is currently a Chancellor's Postdoctoral Research Fellow at School of Electrical, Mechanical and Mechatronic Systems, University of Technology, Sydney (UTS), Sydney, Australia. He is a core member of the Green Energy & Vehicle Innovation Centre (GEVIC) which is one of the Research Strengths at UTS. His current research interests include numerical analysis of electromagnetic field, design and optimization of advanced electrical drive systems for renewable energy systems and applications. Jianguo Zhu received the B.E. from the Jiangsu Institute of Technology, Zhenjiang, China, in 1982, the M.E. from Shanghai University of Technology, Shanghai, China, in 1987, and the Ph.D. from University of Technology Sydney (UTS), Sydney, Australia, in 1995. He is currently a Professor of Electrical Engineering and the Head of the School of Electrical, Mechanical and Mechatronic Systems, UTS. He is the co-director of the Green Energy & Vehicle Innovation Centre (GEVIC) which is one of the Research Strengths at UTS. His research interests include electromagnetics, magnetic properties of materials, electrical machines and drives, power electronics, renewable energy systems, and smart micro-grids. Youguang Guo received the B.E. from Huazhong University of Science and Technology (HUST), Wuhan, China, in 1985, the M.E. from Zhejiang University, Zhejiang, China, in 1988, and the Ph.D. from University of Technology Sydney (UTS), Sydney, Australia in 2004, all in Electrical Engineering. From 1988 to 1998, he lectured in the Department of Electric Power Engineering, HUST. From March 1998 to July 2008, he was a Visiting Research Fellow, Ph.D. candidate, Postdoctoral Fellow, and Research Fellow in the Center for Electrical Machines and Power Electronics, Faculty of Engineering, UTS. He is currently an Associate Professor at the School of Electrical, Mechanical and Mechatronic Systems, UTS. He is a core member of the Green Energy & Vehicle Innovation Centre (GEVIC) which is one of the Research Strengths at UTS. His research fields include measurement and modeling of magnetic properties of magnetic materials, numerical analysis of electromagnetic field, electrical machine design and optimization, power electronic drives and control.

Preface 6
Contents 9
Abbreviations 13
1 Introduction 15
Abstract 15
1.1 Energy and Environment Challenges 15
1.2 Introduction of Electrical Machines, Drive Systems, and Their Applications 17
1.2.1 General Classification of Electrical Machines 17
1.2.2 Electrical Machines and Applications 18
1.3 The State-of-Art Design Optimization Methods for Electrical Machines and Drive Systems 22
1.3.1 Design Optimization of Electrical Machines 22
1.3.2 Design Optimization of Electrical Drive Systems 25
1.3.3 Design Optimization for High Quality Mass Production 28
1.4 Major Objectives of the Book 32
1.5 Organization of the Book 33
References 34
2 Design Fundamentals of Electrical Machines and Drive Systems 39
Abstract 39
2.1 Introduction 39
2.1.1 Framework of Multi-disciplinary Design 39
2.1.2 Power Losses and Efficiency 40
2.2 Electromagnetic Design 43
2.2.1 Analytical Model 43
2.2.2 Magnetic Circuit Model 44
2.2.3 Finite Element Model 47
2.3 Thermal Design 49
2.3.1 Thermal Limits in Electrical Machines 49
2.3.2 Thermal Network Model 50
2.3.3 Finite Element Model 55
2.4 Mechanical Design 57
2.5 Power Electronics Design 59
2.6 Control Algorithms Design 59
2.6.1 Six-Step Control 60
2.6.2 Field Oriented Control 63
2.6.3 Direct Torque Control 66
2.6.4 Model Predictive Control 68
2.6.4.1 One-Step Delay Compensation 71
2.6.4.2 Linear Multiple Horizon Prediction 71
2.6.5 Numerical and Experimental Comparisons of DTC and MPC 72
2.6.5.1 Numerical Simulation 72
2.6.5.2 Experimental Testing 74
2.6.6 Improved MPC with Duty Ratio Optimization 77
2.6.7 Numerical and Experimental Comparisons of DTC and MPC with Duty Ratio Optimization 80
2.6.7.1 Numerical Simulation 80
2.6.7.2 Experimental Test 82
2.7 Summary 83
References 83
3 Optimization Methods 87
Abstract 87
3.1 Introduction 87
3.2 Optimization Algorithms 89
3.2.1 Classic Optimization Algorithms 89
3.2.2 Modern Intelligent Algorithms 90
3.2.2.1 GAs 91
3.2.2.2 DEA 93
3.2.2.3 EDA 95
3.2.2.4 PSO 96
3.3 Multi-objective Optimization Algorithms 98
3.3.1 Introduction to Pareto Optimal Solution 98
3.3.2 MOGA 99
3.3.3 NSGA and NSGA II 101
3.3.4 MPSO 103
3.4 Approximate Models 104
3.4.1 Introduction 104
3.4.2 RSM 104
3.4.3 RBF Model 107
3.4.4 Kriging Model 109
3.4.5 ANN Model 111
3.5 Construction and Verification of Approximate Models 111
3.5.1 DOE Techniques 112
3.5.2 Model Verification 113
3.5.3 Modeling Examples 114
3.6 Summary 117
References 117
4 Design Optimization Methods for Electrical Machines 121
Abstract 121
4.1 Introduction 121
4.2 Classical Optimization Methods 122
4.3 Sequential Optimization Method 123
4.3.1 Method Description 123
4.3.2 Test Example 1---A Mathematical Test Function 128
4.3.3 Test Example 2---Superconducting Magnetic Energy Storage 128
4.3.4 Improved SOM 133
4.3.5 A PM Claw Pole Motor with SMC Stator 135
4.4 Multi-objective Sequential Optimization Method 138
4.4.1 Method Description 139
4.4.2 Example 1---Poloni (POL) Function 141
4.4.3 Example 2---A PM Transverse Flux Machine 143
4.5 Sensitivity Analysis Techniques 145
4.5.1 Local Sensitivity Analysis 146
4.5.2 Analysis of Variance Based on DOE 147
4.5.3 Example Study---A PM Claw Pole Motor 149
4.6 Multi-level Optimization Method 150
4.6.1 Method Introduction 150
4.6.2 Example Study---SMES 152
4.7 Multi-level Genetic Algorithm 153
4.7.1 Problem Matrix 153
4.7.2 Description of MLGA 154
4.7.3 Example Study---SPMSM 156
4.7.3.1 Optimization Model of SPMSM 156
4.7.3.2 Optimization Results and Discussion 158
4.8 Multi-disciplinary Optimization Method 161
4.8.1 Framework of General Multi-disciplinary Optimization 161
4.8.2 Electromagnetic Analysis Based on Molded SMC Core 163
4.8.3 Thermal Analysis with Lumped 3D Thermal Network Model 164
4.8.4 Multi-disciplinary Design Optimization 166
4.8.5 Optimization Results and Discussion 167
4.9 Summary 170
References 171
5 Design Optimization Methods for Electrical Drive Systems 174
Abstract 174
5.1 Introduction 174
5.2 System-Level Design Optimization Framework 176
5.3 Single-Level Design Optimization Method 178
5.4 Multi-level Design Optimization Method 179
5.4.1 Method Flowchart 179
5.4.2 Design Example for a Drive System of TFM and MPC 181
5.4.2.1 Design Optimization Model for Motor Level 181
5.4.2.2 Design Optimization Model for Control Level 182
5.4.2.3 Optimization Flowchart and Results 184
5.5 MLGA for a SPMSM Drive System with FOC 189
5.5.1 Optimization Model 189
5.5.2 Optimization Framework 190
5.5.3 Optimization Results 190
5.6 Summary 192
References 193
6 Design Optimization for High Quality Mass Production 195
Abstract 195
6.1 Introduction 195
6.2 Design for Six-Sigma 198
6.3 Robust Design Optimization of Electrical Machines 202
6.3.1 Single Objective Situation with a PM TFM 202
6.3.1.1 Example on PM-SMC TFM 202
6.3.1.2 Optimization Results and Discussions 203
6.3.2 Multi-objective Optimization with a PM TFM 206
6.3.2.1 Multi-objective Robust Optimization Model 206
6.3.2.2 Improved Multi-objective Sequential Optimization Method 207
6.3.2.3 Optimization Results and Discussion 207
6.4 Robust Design Optimization of Electrical Drive Systems 210
6.4.1 Single-Level Robust Optimization Method 210
6.4.2 Multi-level Robust Optimization Method 211
6.4.2.1 Method Description 211
6.4.2.2 Design Example of a Drive System with TFM and MPC 214
6.5 Summary 223
References 223
7 Application-Oriented Design Optimization Methods for Electrical Machines 226
Abstract 226
7.1 Introduction 226
7.2 Application-Oriented Design Optimization Method 227
7.2.1 Method Description 227
7.2.2 An Optimal PM-SMC Machine for a Refrigerator 229
7.3 Robust Approach for the Application-Oriented Design Optimization Method 233
7.3.1 Method Description 233
7.3.2 An Optimal FSPMM for a PHEV Drive 233
7.3.2.1 FSPMMs and Topologies 233
7.3.2.2 Qualitative Analysis Based on Size Equation 240
7.3.2.3 Quantitative Analysis Based on Optimization 240
7.4 Summary and Remarks 243
References 244
8 Conclusions and Future Works 247
Abstract 247
8.1 Conclusions 247
8.2 Future Works 249