报 告 人：雷刚，澳大利亚悉尼科技大学博士
报告题目：Efficient Optimization Methods for Robust Design of Electrical Machines
内容简介：There are many uncertainties, such as material diversities and manufacturing tolerances, in the practical production of electrical machines. The performance and quality of batch produced electrical machines depend highly on these uncertainties. To reduce the effects of these uncertainties, robust design optimization methods should be investigated. There are three popular robust design approaches, the Taguchi parameter design, worse-case design and design for six-sigma. However, there is a major challenge for these methods under the high-dimensional situation, the huge computation cost for the evaluation of robustness of many design candidates. To attempt this challenge, a space reduction optimization strategy will be introduced in this talk, which will greatly improve the optimization efficiency of robust design of electrical machines.
报告人简介：Dr. Gang Lei is a senior lecturer at the School of Electrical and Data Engineering, University of Technology Sydney (UTS), Australia. He received the PhD degree in Electrical Engineering from Huazhong University of Science and Technology, China, in June 2009. He has been previously employed at UTS as a Chancellor’s Postdoctoral Research Fellow.
Dr. Lei’s major research area covers the multidisciplinary design optimization of electrical drive and renewable energy systems, including electromagnetics, thermotics, mechanics, power electronics, applied mathematics, and quality control. Based on the research findings, he has published over 100 journal and conference papers (including over 55 IEEE Transactions articles), and a research book entitled “Multidisciplinary Design Optimization Methods for Electrical Machines and Drive Systems” by Springer in 2016. Currently, he is an associate editor of IEEE Transactions on Industrial Electronics and a guest editor for a special section on the robust design optimization of electrical machines and drives in IEEE Transactions on Energy Conversion.