基于OT-IRM算法的棒-板长间隙操作冲击击穿电压海拔校正

With increasing altitude, the insulation strength of air gap decreases. Currently, research on gap discharge is primarily concentrated in low-altitude regions, lacking experimental and theoretical support for external insulation design of electrical equipment at high altitudes. To investigate the long-gap discharge characteristics at high altitudes, this study conducted experiments to obtain the switching impulse discharge characteristic curves of rod-plane gap at altitudes of 55 m, 2500 m, and 4300 m. In response to the distribution characteristics of the experimental data, we propose an invariant risk minimization neural network ensemble algorithm based on optimal transport. Based on experimental data, a breakdown voltage prediction model applicable to different altitudes was established. The model achieved an average error of 2.3% on the test set, validating its high accuracy and generalization. Additionally, the computational results of the proposed model were compared with existing altitude correction methods and other machine learning models, further validating its effectiveness. Finally, the model was utilized to obtain 50% breakdown voltage under typical meteorological conditions at different altitudes. The altitude correction method proposed in this paper can accommodate a wide range of climatic variations, thus providing valuable reference for the construction of high-altitude power grids.