A Posteriori Strategy to Identify Robust Solutions in the Many-Objective Design Optimization of an Axial-Flux Permanent Magnet Synchronous Generator

Abstract

Parametric uncertainties inherently exist in most real-world equipment design. In a generator design optimization process, parametric uncertainties are an important issue given the negative impact they have on generator’s efficiency. In this work, the NSGA-III algorithm is used to obtain a set of robust optimum designs of an Axial-Flux Permanent Magnet Synchronous Generator subjected to parametric uncertainties, and thereafter an a posteriori strategy is applied in the set in order to identify the best robust solution which has low parametric sensitivity and high nominal efficiency to be implemented in practice. This methodology was developed with the aim to select the best robust optimum solution from the robust Pareto-optimal set in a many-objective environment.