Corona onset and breakdown voltage prediction of rod-plane air gaps based on SVM algorithm

Abstract

Corona onset voltage and breakdown voltage of the air gap are the basis for the external insulation design of high-voltage transmission projects. A new prediction method for the discharge voltage of rod-plane air gaps is proposed in this paper. Support vector machine (SVM) is applied to establish the prediction model, and the improved grid search (GS) method is used for parameter optimization. The features extracted from the electric field distribution calculated by finite element model of the rod-plane air gap are taken as the input parameters to the SVM model, and whether corona will onset, or the gap will breakdown under a given voltage is taken as the output of the SVM model. Trained by the electric field features under several limited experimental values, the SVM model is effective to predict the corona onset or breakdown voltage. The proposed method is applied to predict the positive DC corona onset voltage and power frequency AC breakdown voltage of rod-plane air gaps. The predicted results are in accordance with the experimental values with small deviation, which preliminary validate the feasibility of predicting the discharge voltage of the air gap by machine learning algorithms.