Modern Electrical Machine Design Optimization: Techniques, Trends, and Best Practices

被引:183
作者
Bramerdorfer, Gerd [1 ]
Tapia, Juan A. [2 ]
Pyrhonen, Juha J. [3 ]
Cavagnino, Andrea [4 ]
机构
[1] Johannes Kepler Univ Linz, A-4040 Linz, Austria
[2] Univ Concepcion, Concepcion 4070386, Chile
[3] Lappeenranta Univ Technol, Lappeenranta 53850, Finland
[4] Politecn Torino, I-10129 Turin, Italy
关键词
Electric machines; evolutionary computation; genetic algorithms (GAs); metamodeling; multidimensional systems; optimization; Pareto optimization; particle swarm optimization (PSO); reliability; robustness; MAGNETIC-PROPERTIES; GENETIC ALGORITHM; INDUCTION-MOTOR; TORQUE; ROTOR; IDENTIFICATION; EFFICIENCY; SYSTEM; STATOR;
D O I
10.1109/TIE.2018.2801805
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Disruptive innovations in electrical machine design optimization are observed in this paper, motivated by emerging trends. Improvements in mathematics and computer science enable more detailed optimization scenarios that cover evermore aspects of physics. In the past, electrical machine design was equivalent to investigating the electromagnetic performance. Nowadays, thermal, rotor dynamics, power electronics, and control aspects are included. The material and engineering science have introduced new dimensions on the optimization process and impact of manufacturing, and unavoidable tolerances should be considered. Consequently, multifaceted scenarios are analyzed and improvements in numerous fields take effect. This paper is a reference for both academics and practicing engineers regarding recent developments and future trends. It comprises the definition of optimization scenarios regarding geometry specification and goal setting. Moreover, a materials-based perspective and techniques for solving optimization problems are included. Finally, a collection of examples from the literature is presented and two particular scenarios are illustrated in detail.
引用
收藏
页码:7672 / 7684
页数:13
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