Intelligent Prediction of Dielectric Strength for Long Air Gaps at High Altitudes Based on Regularization‐Logistic Regression

Air discharge originates from the interaction between electric field (EF) and the meteorological environment, and the dielectric strength of a long air gap is affected by both EF distribution and atmospheric parameters. A regularization‐logistic regression (R‐LR) model is proposed for the accurate calculation of long air gap discharge voltage at high altitudes. Using 4 meteorological features and 9 EF features as inputs for the R‐LR model, the R‐LR model was trained using discharge test data from rod‐plane air gaps at altitudes of 55–4300 m. The trained model was used to predict the dielectric strength of rod‐plane air gaps at an altitude of 5000 m. The prediction results of the R‐LR model were compared with those of random forest (RF), support vector classifier (SVC) and k ‐nearest neighbor (KNN) models. The results showed that the accuracy and generalization of the R‐LR model were better than those of the other models. The MAPE of the R‐LR model on the test set was 1.32%. The model was validated by using the rod‐plane air gap discharge test data under different meteorological environments in a plain area. The predicted discharge voltage values were basically consistent with the test values, further demonstrating the effectiveness and generalizability of the model. This study can offer a reference for predicting the dielectric strength of air gaps under different meteorological environments in high altitude areas. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.