Prediction Algorithm for Long Air Gap Discharge Voltage Under Positive Polarity Operation Shock

To quickly and accurately predict the discharge voltage of long air gaps under positive polarity operation impact, this study proposes an iterative prediction algorithm that integrates KNN algorithm and physical feature modeling. By establishing a discharge process model that includes corona initiation, streamer development, leading initiation, and final jump, extracting electric field distribution characteristics, and using KNN classification combined with interval iterative approximation to achieve high-precision prediction of discharge voltage. The experiment shows that the predicted results of this method are highly consistent with the actual values, with small errors and strong generalization ability.