Multi-objective design optimization using dual-level response surface methodology and booth's algorithm for permanent magnet synchronous generators

• Main Authors: Asef, Pedram, Bargalló Perpiñá, Ramón, Barzegaran, M.R, Lapthorn, Andrew, Mewes, Daniela
• Format: Publication
• Language: English
• Published: 22-11-2017
• Subjects: Algorithm design and analysis; Booths Algorithm; Charge coupled devices; Design optimization; Dual Response Surface Methodology; Elements finits, Mètode dels; Finite Element Analysis; Finite element method; Matemàtiques i estadística; Multi-Objective Optimization; Power generation; Robustness; Stators; Synchronous Machine; Àrees temàtiques de la UPC
• ISSN: 0885-8969
• DOI: 10.1109/TEC.2017.2777397

This paper studies a dual-level response surface methodology (DRSM) coupled with Booth's algorithm using a simulated annealing (BA-SA) method as a multi-objective technique for parametric modeling and machine design optimization for the first time. The aim of the research is for power maximization and cost of manufacture minimization resulting in a highly optimized wind generator to improve small power generation performance. The DRSM is employed to determine the best set of design parameters for power maximization in a surface-mounted permanent magnet synchronous generator (SPMSG) with an exterior-rotor topology. Additionally, the BA-SA method is investigated to minimize material cost while keeping the volume constant. DRSM by different design functions including mixed resolution robust design (MR-RD), full factorial design (FFD), central composite design (CCD), and box-behnken design (BBD) are applied to optimize the power performance resulting in very small errors. An analysis of the variance via multi-level RSM plots is used to check the adequacy of fit in the design region and determines the parameter settings to manufacture a high-quality wind generator. The analytical and numerical calculations have been experimentally verified and have successfully validated the theoretical and multi-objective optimization design methods presented.
Peer Reviewed