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

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