Ultra‐high‐dimensional multi‐level optimisation strategies for electrical machines

Abstract

Abstract Electrical machine optimisation is normally a high‐dimensional non‐linear multi‐objective optimisation problem. A multi‐level optimisation (MO) strategy is currently used to improve efficiency, where sensitivity analysis is required for dividing design parameters into different groups. However, the conventional MO strategy cannot handle ultra‐high‐dimensional optimisation problems. In this paper, a sensitivity analysis method with variable weighted intervals is proposed to calculate the sensitivity coefficient in the parameter design range. Moreover, three improved multi‐level optimisation strategies based on different optimisation algorithms, sequential sensitivity strategies, and machine learning models are proposed, analysed, and compared with the conventional MO strategy. Through a case study of a synchronous reluctance machine, it can be seen that the proposed optimisation strategies can improve the optimisation results and efficiency of ultra‐high‐dimensional optimisation of electrical machines.

Keywords

• design
• learning (artificial intelligence)
• optimisation
• sensitivity analysis
• synchronous machines