Hierarchical Optimization Method Research For A Double-stator Tubular Linear Machine

The tubular linear machine with double-stator structure not only eliminates the lateral end effect,but also has higher space utilization and power density than the single sided tubular machine.It has great application potential in high-end manufacturing equipment and wave energy conversion systems.However,the cogging effect formed by the double-stator structure adds up to make the thrust ripple more significant.In order to solve this problem,this paper takes the double-stator tubular linear machine as the research object,proposes a hierarchical,less sample multi-objective optimization method,and carries out global optimization design for the machine structural parameters;In order to improve the robustness of the designed machine performance,6σ robust method is used to optimize the parameters with high sensitivity.Major works are as follows:Based on electromagnetic characteristic analysis and topology comparison,determine the excitation topology method of the double-stator tubular linear machine.The comparison results confirm that compared to non-segmented,segmented single excitation,and segmented hybrid excitation,segmented hybrid excitation exhibits better air gap magnetic density under the same permanent magnet material conditions.The hierarchical optimization of the double-stator tubular linear machine with segmented hybrid excitation is carried out.Firstly,the structural parameters are pre distinguished using the comprehensive sensitivity method,and then cross factor analysis is used to verify the interaction effects between the parameters.The structural parameters are divided into three levels: high,medium,and low sensitivity;The three layers were modeled using sequential Kriging,Gaussian Process Regression(GPR),and quadratic polynomials,respectively.The final model is optimized using Non Dominated Sorting Genetic Algorithm II(NSGA-II);In the sequential optimization algorithm,a parallel addition strategy is also adopted,which strengthens global and local optimization and improves optimization efficiency;Finally,the average thrust is increased by 20.7%,the thrust fluctuation is reduced by 50.4%,and the total harmonic distortion of voltage is reduced by 13.7%.The results show that the proposed hierarchical optimization algorithm is feasible.Due to the significance of high-sensitivity parameters and considering that perturbation error will produce large performance fluctuations on motor manufacturing,robust optimization of high-sensitivity parameters is carried out.Based on the surrogate model of deterministic optimization,6σ level design is introduced and NSGA-Ⅱalgorithm is combined to carry out robust optimization design.The reliability is improved by 14.7%,and the performance fluctuation is less than that of deterministic optimization when disturbed by the same error,which indicates that the robust optimization results have good robustness.By performance comparison,both deterministic optimization and robust optimization are improved compared with before optimization,but robust optimization results are worse than deterministic optimization results,indicating that robust optimization results and deterministic optimization results are not consistent.The experimental platform measured the cogging force and output voltage of the motor before optimization,and calculated the output voltage at load before optimization through finite element simulation,as well as the cogging force and output voltage at idle and load after optimization.Experiments and simulations verified the effectiveness of the optimization results.