ebook ebooks e-book e-books downloaden bei MyEbooks.ch downloaden

Multidisciplinary Design Optimization Methods for Electrical Machines and Drive Systems

:	Gang Lei, Jianguo Zhu, Youguang Guo
:	Multidisciplinary Design Optimization Methods for Electrical Machines and Drive Systems
:	Springer-Verlag
:	9783662492710
:	1
:	CHF 84.20
:

:	Elektronik, Elektrotechnik, Nachrichtentechnik
:	English

:	251
:	DRM
:	PC/MAC/eReader/Tablet
:	PDF

This book presents various computationally efficient component- and system-level design optimization methods for advanced electrical machines and drive systems. Readers will discover novel design optimization concepts developed by the authors and other researchers in the last decade, including application-oriented, multi-disciplinary, multi-objective, multi-level, deterministic, and robust design optimization methods. A multi-disciplinary analysis includes various aspects of materials, electromagnetics, thermotics, mechanics, power electronics, applied mathematics, manufacturing technology, and quality control and management. This book will benefit both researchers and engineers in the field of motor and drive design and manufacturing, thus enabling the effective development of the high-quality production of innovative, high-performance drive systems for challenging applications, such as green energy systems and electric vehicles.

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