Multi‐objective comprehensive teaching algorithm for multi‐objective optimisation design of permanent magnet synchronous motor. Issue 13 (12th February 2021)

• Record Type: Journal Article
• Authors: Sun, Changle; Wen, Feng; Xiong, Wei; Wang, Haitao; Shang, Hongxu
• Is Part Of: IET electric power applications. Volume 14:Issue 13(2020)
• Page Start: 2564
• Page End: 2576
• Publication Date: 2021-02-12
• Subjects: synchronous motors -- finite element analysis -- Pareto optimisation -- equivalent circuits -- design engineering -- permanent magnet motors -- optimisation -- genetic algorithms; multiobjective comprehensive teaching algorithm -- multiobjective optimisation design -- permanent magnet synchronous motor -- multiobjective problem -- PMSM optimisation -- teaching–learning‐based optimisation algorithm -- multivariate optimisation -- magnetic equivalent circuit -- radial magnetic field PMSM -- design requirements -- PMSM multiobjective optimisation calculation processes -- natural optimisation algorithms -- optimisation results -- PMSM multiobjective structure optimisation design; Electric power -- Periodicals; Electric power systems -- Periodicals; 621.305
• DOI: 10.1049/iet-epa.2020.0322
• Languages: English
• ISSNs: 1751-8660

Abstract

Abstract : The design of permanent magnet synchronous motor (PMSM) is a constrained multi‐objective problem, and there are often conflicts between the goals. Aiming at the complexity of PMSM optimisation and a large number of variables, multi‐objective optimisation design of PMSM using a multi‐objective comprehensive teaching algorithm (MCTA) is proposed. This algorithm is improved based on the teaching–learning‐based optimisation algorithm and can better adapt to large‐scale sample space and multivariate optimisation. Also, based on a magnetic equivalent circuit, a static electromagnetic model of the radial magnetic field PMSM is derived, and the design requirements and goals of the PMSM are analysed. In the two PMSM multi‐objective optimisation calculation processes, the optimal solution was calculated based on the composite fitness function and Pareto front, respectively. The results of the MCTA were compared with other natural optimisation algorithms. Finally, according to the optimisation results of two sets of experiments, the correctness of the optimal solution of the algorithm was verified by finite element analysis and actual measurement of the prototype. The analysis of results shows the effectiveness, simplicity, and inspiration of MCTA in the PMSM multi‐objective structure optimisation design.