A Hybrid LHS–RSM Optimization Framework for Parameter Selection in Dielectric Gradient Topology Design

Topology optimization has been widely applied to dielectric graded insulation design in gas-insulated switchgear (GIS); however, the selection of optimization parameters remains challenging due to strong coupling among design variables and the high computational cost of conventional parametric scanning methods. To address this issue, a hybrid optimization framework integrating Latin hypercube sampling (LHS) and response surface methodology (RSM) is proposed for efficient parameter selection in dielectric gradient topology design. The proposed framework combines global parameter space exploration, parameter space reduction, and multi-stage response surface optimization to construct surrogate models for efficient parameter optimization. The results show that the maximum electric field of the optimized insulator is reduced from 3.336 kV/mm to 1.400 kV/mm, demonstrating the effectiveness of the proposed method in improving electric field uniformity and optimization efficiency.