Switching impulse discharge voltage prediction of EHV and UHV transmission lines–tower air gaps by a support vector classifier

Discharge voltage prediction of practical air gaps in transmission projects is a long-sought goal and also a great challenge in high-voltage (HV) engineering. An approach combined electric field simulation, feature extraction and machine learning algorithm is presented in this study to predict the switching impulse discharge voltages of extra-HV (EHV) and ultra-HV (UHV) transmission lines-tower air gaps. Some features extracted from the electrostatic field distribution are used to characterise the air-gap configuration and taken as input parameters of a prediction model established by a support vector classifier (SVC). Three kinds of actual gap configurations in EHV and UHV transmission lines are taken as test samples to validate the validity of the SVC model. Trained by experimental data of rod-plane gaps and one of the engineering gap configurations, this model is able to predict the discharge voltages of the other two conductor-tower gaps with acceptable accuracy. The mean absolute percentage errors of the three prediction results are 6.84, 4.19 and 3.46%. This research demonstrates the feasibility of discharge voltage prediction for complicated engineering gaps, which is useful to reduce the costly full-scale tests and helpful to guide the external insulation design.