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E-grāmata: Multidisciplinary Design Optimization Methods for Electrical Machines and Drive Systems

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  • Formāts: PDF+DRM
  • Sērija : Power Systems
  • Izdošanas datums: 05-Feb-2016
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783662492710
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Multidisciplinary Design Optimization Methods for Electrical Machines and Drive SystemsPalielināt
  • Formāts: PDF+DRM
  • Sērija : Power Systems
  • Izdošanas datums: 05-Feb-2016
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Valoda: eng
  • ISBN-13: 9783662492710
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This book presents various computationally efficient component- andsystem-level design optimization methods for advanced electrical machines anddrive systems. Readers will discover novel design optimization conceptsdeveloped by the authors and other researchers in the last decade, includingapplication-oriented, multi-disciplinary, multi-objective, multi-level, deterministic,and robust design optimization methods. A multi-disciplinary analysis includesvarious aspects of materials, electromagnetics, thermotics, mechanics, powerelectronics, applied mathematics, manufacturing technology, and quality controland management. This book will benefit both researchers and engineers in thefield of motor and drive design and manufacturing, thus enabling the effectivedevelopment of the high-quality production of innovative, high-performancedrive systems for challenging applications, such as green energy systems andelectric vehicles.

Introduction.- Design fundamentals of electrical machines and drive systems.- Optimization methods.- Design optimization methods for electrical machines.- Design optimization methods for electrical drive systems.- Design optimizationfor high quality mass production.- Application-oriented design optimization methods forelectrical machines.- Conclusion and future works.
1 Introduction
1(24)
1.1 Energy and Environment Challenges
1(2)
1.2 Introduction of Electrical Machines, Drive Systems, and Their Applications
3(5)
1.2.1 General Classification of Electrical Machines
3(1)
1.2.2 Electrical Machines and Applications
4(4)
1.3 The State-of-Art Design Optimization Methods for Electrical Machines and Drive Systems
8(10)
1.3.1 Design Optimization of Electrical Machines
8(3)
1.3.2 Design Optimization of Electrical Drive Systems
11(3)
1.3.3 Design Optimization for High Quality Mass Production
14(4)
1.4 Major Objectives of the Book
18(1)
1.5 Organization of the Book
19(6)
References
20(5)
2 Design Fundamentals of Electrical Machines and Drive Systems
25(48)
2.1 Introduction
25(4)
2.1.1 Framework of Multi-disciplinary Design
25(1)
2.1.2 Power Losses and Efficiency
26(3)
2.2 Electromagnetic Design
29(6)
2.2.1 Analytical Model
29(1)
2.2.2 Magnetic Circuit Model
30(3)
2.2.3 Finite Element Model
33(2)
2.3 Thermal Design
35(8)
2.3.1 Thermal Limits in Electrical Machines
35(1)
2.3.2 Thermal Network Model
36(5)
2.3.3 Finite Element Model
41(2)
2.4 Mechanical Design
43(2)
2.5 Power Electronics Design
45(1)
2.6 Control Algorithms Design
45(24)
2.6.1 Six-Step Control
46(3)
2.6.2 Field Oriented Control
49(3)
2.6.3 Direct Torque Control
52(2)
2.6.4 Model Predictive Control
54(4)
2.6.5 Numerical and Experimental Comparisons of DTC and MPC
58(5)
2.6.6 Improved MPC with Duty Ratio Optimization
63(3)
2.6.7 Numerical and Experimental Comparisons of DTC and MPC with Duty Ratio Optimization
66(3)
2.7 Summary
69(4)
References
69(4)
3 Optimization Methods
73(34)
3.1 Introduction
73(2)
3.2 Optimization Algorithms
75(9)
3.2.1 Classic Optimization Algorithms
75(1)
3.2.2 Modern Intelligent Algorithms
76(8)
3.3 Multi-objective Optimization Algorithms
84(6)
3.3.1 Introduction to Pareto Optimal Solution
84(1)
3.3.2 MOGA
85(2)
3.3.3 NSGA and NSGA II
87(2)
3.3.4 MPSO
89(1)
3.4 Approximate Models
90(7)
3.4.1 Introduction
90(1)
3.4.2 RSM
90(3)
3.4.3 RBF Model
93(2)
3.4.4 Kriging Model
95(2)
3.4.5 ANN Model
97(1)
3.5 Construction and Verification of Approximate Models
97(6)
3.5.1 DOE Techniques
98(1)
3.5.2 Model Verification
99(1)
3.5.3 Modeling Examples
100(3)
3.6 Summary
103(4)
References
103(4)
4 Design Optimization Methods for Electrical Machines
107(54)
4.1 Introduction
107(1)
4.2 Classical Optimization Methods
108(1)
4.3 Sequential Optimization Method
109(15)
4.3.1 Method Description
109(5)
4.3.2 Test Example 1---A Mathematical Test Function
114(1)
4.3.3 Test Example 2---Superconducting Magnetic Energy Storage
114(5)
4.3.4 Improved SOM
119(2)
4.3.5 A PM Claw Pole Motor with SMC Stator
121(3)
4.4 Multi-objective Sequential Optimization Method
124(7)
4.4.1 Method Description
125(2)
4.4.2 Example 1---Poloni (POL) Function
127(2)
4.4.3 Example 2---A PM Transverse Flux Machine
129(2)
4.5 Sensitivity Analysis Techniques
131(5)
4.5.1 Local Sensitivity Analysis
132(1)
4.5.2 Analysis of Variance Based on DOE
133(2)
4.5.3 Example Study---A PM Claw Pole Motor
135(1)
4.6 Multi-level Optimization Method
136(3)
4.6.1 Method Introduction
136(2)
4.6.2 Example Study---SMES
138(1)
4.7 Multi-level Genetic Algorithm
139(8)
4.7.1 Problem Matrix
139(1)
4.7.2 Description of MLGA
140(2)
4.7.3 Example Study---SPMSM
142(5)
4.8 Multi-disciplinary Optimization Method
147(9)
4.8.1 Framework of General Multi-disciplinary Optimization
147(2)
4.8.2 Electromagnetic Analysis Based on Molded SMC Core
149(1)
4.8.3 Thermal Analysis with Lumped 3D Thermal Network Model
150(2)
4.8.4 Multi-disciplinary Design Optimization
152(1)
4.8.5 Optimization Results and Discussion
153(3)
4.9 Summary
156(5)
References
157(4)
5 Design Optimization Methods for Electrical Drive Systems
161(22)
5.1 Introduction
161(2)
5.2 System-Level Design Optimization Framework
163(2)
5.3 Single-Level Design Optimization Method
165(1)
5.4 Multi-level Design Optimization Method
166(10)
5.4.1 Method Flowchart
166(2)
5.4.2 Design Example for a Drive System of TFM and MPC
168(8)
5.5 MLGA for a SPMSM Drive System with FOC
176(3)
5.5.1 Optimization Model
176(1)
5.5.2 Optimization Framework
177(1)
5.5.3 Optimization Results
177(2)
5.6 Summary
179(4)
References
180(3)
6 Design Optimization for High Quality Mass Production
183(32)
6.1 Introduction
183(3)
6.2 Design for Six-Sigma
186(4)
6.3 Robust Design Optimization of Electrical Machines
190(8)
6.3.1 Single Objective Situation with a PM TFM
190(4)
6.3.2 Multi-objective Optimization with a PM TFM
194(4)
6.4 Robust Design Optimization of Electrical Drive Systems
198(13)
6.4.1 Single-Level Robust Optimization Method
198(1)
6.4.2 Multi-level Robust Optimization Method
199(12)
6.5 Summary
211(4)
References
211(4)
7 Application-Oriented Design Optimization Methods for Electrical Machines
215(22)
7.1 Introduction
215(1)
7.2 Application-Oriented Design Optimization Method
216(6)
7.2.1 Method Description
216(2)
7.2.2 An Optimal PM-SMC Machine for a Refrigerator
218(4)
7.3 Robust Approach for the Application-Oriented Design Optimization Method
222(10)
7.3.1 Method Description
222(1)
7.3.2 An Optimal FSPMM for a PHEV Drive
222(10)
7.4 Summary and Remarks
232(5)
References
233(4)
8 Conclusions and Future Works
237
8.1 Conclusions
237(2)
8.2 Future Works
239
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.
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