Application of GWO-SVM Algorithm in Arc Detection of Pantograph

Abstract

High-speed train will produce pantograph arc during the driving process, which is harmful to pantograph-catenary system. In order to reduce pantograph-catenary system damage, a method based on Gray Wolf algorithm to optimize the binary Support Vector Machine classifier to identify pantograph arc is proposed. In this article, 5 groups of pantograph current experiments under different conditions are carried out, and the current data in the pantograph-catenary system under different conditions are measured. The current data state obtained from the pantograph experiments is divided into normal current state and arc current state. Select the mean value, variance, standard deviation, mean value of the first-order difference, and mean value of the second-order difference of the current data as the characteristic value of the pantograph current, and calculate the contribution rate of each characteristic value at the same time, then the current eigenvalue data with a high contribution rate is used as a training sample for learning and recognition through the classifier optimized by Gray Wolf algorithm. The experimental results show that the Gray Wolf optimization algorithm can quickly and accurately identify the pantograph arc, and the classification model obtained is more accurate than the commonly used optimization algorithms such as genetic algorithm and particle swarm. In addition, an engineering implementation of on-line identification of pantograph arc based on industrial computer is proposed.

Keywords

• Characteristic value
• classification model
• contribution rate
• Gray Wolf algorithm
• high-speed train
• industrial computer