Multi-objective optimization of a double-sided air-cored tubular generator based on a new integrated modeling method

Abstract

Double-sided air-cored tubular generators (DSTG) have higher power density than traditional air-cored generators, and are desirable for applications in renewable energy conversion systems. In order to achieve best power quality and maximum efficiency with minimum cost, multi-objective optimization of the DSTG is carried out. Aim to decrease the computational time and guarantee the accuracy of the multi-objective optimization of DSTGs, a new integrated modeling method is proposed and focused in this paper. The new modeling method integrates the analytical models and the machine learning models together. The experimental results prove that the new integrated model can provide higher accurate calculation results than analytical models and need fewer samples than machine learning models. The optimization time needed by the new model is 5 times shorter than that needed by the FE model.