Intelligent computing of positive switching impulse breakdown voltage of rod-plane air gap based on extremely randomized trees algorithm

Abstract

The insulating strength of an air gap is an important basis for external insulation design of power transmission and transformation project, and both gap structure and meteorological conditions have a great influence on the breakdown voltage of air gap. The temperature, humidity and other meteorological conditions in the actual operating environment of power transmission and transformation projects may change greatly with time, so presently, there is no better method to calculate the breakdown voltage of air gaps in various meteorological conditions. In this paper, a prediction model for positive switching impulse breakdown voltage of rod-plane air gap based on extremely randomized trees is proposed. All samples are divided into training set and test set by Self-Organizing Mapping neural network and stratified sampling method. In the test set, the predicted breakdown voltage results of rod-plane air gap based on decision trees, random forest, extremely randomized trees and support vector machine are compared, with reference to the rectification results of atmospheric correction method. The result shows that the maximum absolute relative error of the model is 4.3% and the mean absolute percentage error is 1.6%. Then, the importance of features of import variables is calculated with the trained extremely randomized trees model. Finally, the breakdown voltage of rod-plane air gap in correspondence to different gap distances in four extreme meteorological conditions is calculated with the model. The method can provide reference for calculation of the breakdown voltage of air gap in various meteorological conditions.