Constrained Multi-Objective Optimization for Double-Sided Tubular Machine With Hybrid Segmented PM

Abstract

Double-sided tubular machines (DSTM) have greatly improved space utilization and power density, and are widely used in tidal power generation and high-power electric drives, while the thrust force ripple is also significant. To get the minimum cogging force with the maximum thrust force, hybrid segmented PM array topology is introduced to the DSTM and used as research object for multi-objective optimization. Due to the constraints among the parameters to be optimized, this paper proposes a new constrained multi-parameter multi-objective optimization method. In this method, a new optimal Latin hypercube(OLH) is put forward to solve the constrained parameter sampling problem. RReliefF is used to distinguish the importance of multi-parameter and divide them into three layers: strong, medium and weak, and surrogate models are established hierarchically to simplify the optimization complexity. In addition, an improved Gaussian process regression(GPR) algorithm is proposed to improve the optimization accuracy of strong significance parameters. The optimization results show that the average thrust force is increased by 4.7%, the cogging force is reduced by 28%, and total harmonic distortion rate of the voltage is reduced by 30.5% without loss of efficiency, which significantly improves the performance of the machine, and verifies the effectiveness of the proposed method, which is suitable for constrained multi-parameter multi-objective optimization.