Large multidisciplinary design optimization applied to a permanent magnet synchronous generator

被引:3
作者
Bazzo, T. P. M. [1 ,2 ]
Gerbaud, L. [2 ]
Kolzer, J. F. [1 ]
Carlson, R. [1 ]
Wurtz, F. [2 ]
机构
[1] Univ Fed Santa Catarina, Dept Elect Engn, GRUCAD, Campus Reitor Joao David Ferreira Lima, Florianopolis, SC, Brazil
[2] Grenoble Alpes Univ, G2Elab, Grenoble, France
关键词
Large optimization problem; multidisciplinary design optimization; permanent magnet synchronous generator design; sequential quadratic algorithm; wind power; MODELS;
D O I
10.3233/JAE-172287
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The present paper deals with a large multidisciplinary design optimization (MDO) applied to a wind turbine permanent magnet synchronous generator (PMSG). The multidisciplinary nature of the model allows reliable results, but it demands high complexity and a large number of variables. Furthermore, to calculate the wind turbine energy production it is necessary to include several operating points in the model, increasing even more the dimension of the problem. To deal with this large optimization problem, the sequential quadratic programming (SQP) algorithm has been chosen. The results show that, besides the model complexity, the solution is obtained fast.
引用
收藏
页码:S171 / S181
页数:11
相关论文
共 50 条
[31]   Riemannian optimization and multidisciplinary design optimization [J].
Bakker, Craig ;
Parks, Geoffrey T. .
OPTIMIZATION AND ENGINEERING, 2016, 17 (04) :663-693
[32]   Design and Characteristic Investigation of Novel Dual-Stator V-Shaped Magnetic Pole Six-Phase Permanent Magnet Synchronous Generator for Wind Power Application [J].
Kumar, Raja Ram ;
Chetri, Chandan ;
Devi, Priyanka ;
Saket, Ram Khelawan ;
Blaabjerg, Frede ;
Sanjeevikumar, Padmanaban ;
Holm-Nielsen, Jens Bo .
ELECTRIC POWER COMPONENTS AND SYSTEMS, 2020, 48 (14-15) :1537-1550
[33]   Optimal Parameter Design for Permanent-Magnet Synchronous Generators Used in the Small-Scale Wind Powers [J].
Huang, Chung-Neng ;
Lin, David T. W. ;
Chang, Chong-Ching .
ADVANCED SCIENCE LETTERS, 2011, 4 (4-5) :1607-1610
[34]   MULTIDISCIPLINARY DESIGN OPTIMIZATION OF A SAILPLAN [J].
Peri, Daniele ;
Parolini, Nicola ;
Fossati, Fabio .
COMPUTATIONAL METHODS IN MARINE ENGINEERING VI (MARINE 2015), 2015, :177-187
[35]   Adaptive PIF Control for Permanent Magnet Synchronous Motors Based on GPC [J].
Lu, Shaowu ;
Tang, Xiaoqi ;
Song, Bao .
SENSORS, 2013, 13 (01) :175-192
[36]   Metamodel Assisted Multidisciplinary Design Optimization for Satellite with a Large-Size Payload [J].
Tai, Xinhui ;
Shi, Renhe ;
Chen, Yujun ;
Long, Teng ;
Ye, Nianhui .
PROCEEDINGS OF 2022 INTERNATIONAL CONFERENCE ON AUTONOMOUS UNMANNED SYSTEMS, ICAUS 2022, 2023, 1010 :2611-2624
[37]   APPLICATION OF HYBRID GENETIC ALGORITHM IN AEROELASTIC MULTIDISCIPLINARY DESIGN OPTIMIZATION OF LARGE AIRCRAFT [J].
唐长红 ;
万志强 .
Transactions of Nanjing University of Aeronautics and Astronautics, 2013, (02) :109-117
[38]   Towards an efficient global multidisciplinary design optimization algorithm [J].
Dubreuil, S. ;
Bartoli, N. ;
Gogu, C. ;
Lefebvre, T. .
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2020, 62 (04) :1739-1765
[39]   Multidisciplinary design optimization in design for additive manufacturing [J].
Liu, Guang ;
Xiong, Yi ;
Rosen, David W. .
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, 2021, 9 (01) :128-143
[40]   Latin hypercube sampling applied to reliability-based multidisciplinary design optimization of a launch vehicle [J].
Roshanian, Jafar ;
Ebrahimi, Masoud .
AEROSPACE SCIENCE AND TECHNOLOGY, 2013, 28 (01) :297-304