Reasonable arrangement of the structure of the interrupter is an effective way to avoid insulation damage. A structural optimization method based on neural network and genetic algorithm was proposed to optimize the insulation performance of vacuum interrupter. The electric field model of 12 kV vacuum interrupter was built. The electric field distribution of the main shield with different parameters was calculated. The orthogonal regression method and BP neural network were combined to construct the mapping model of the structural parame

ters of the main shield and the maximum field strength and field strength uniformity of the vacuum interrupter. The genetic algorithm was introduced to optimize the structure of the main shield. The influence of shielding material on the insulation performance of vacuum interrupter was analyzed. The results show that the electric field of the vacuum interrupter decreases with the increase of the diameter of the shield

and increases with the increase of the thickness of the shield

and decreases with the increase of the angle. Stainless steel as the main shield material can make the vacuum interrupter have the advantages of low cost and excellent insulation performance. The maximum electric field of vacuum interrupter is reduced by 2.07×10

6

V/m

and the uniformity of electric field at shield and contact is increased by 1.4×10

4

V/m after optimization. The optimization design method can reasonably improve the structural of vacuum interrupter and effectively control the electric field distribution of vacuum interrupter.