Application of GWO-SVM Algorithm in Arc Detection of Pantograph

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...

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Autores principales: Bin Li, Chenyu Luo, Zhiyong Wang
Formato: article
Lenguaje:EN
Publicado: IEEE 2020
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Acceso en línea:https://doaj.org/article/c243c0d53c4d4868bd31cd4b3aab388d
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spelling oai:doaj.org-article:c243c0d53c4d4868bd31cd4b3aab388d2021-11-19T00:06:37ZApplication of GWO-SVM Algorithm in Arc Detection of Pantograph2169-353610.1109/ACCESS.2020.3025714https://doaj.org/article/c243c0d53c4d4868bd31cd4b3aab388d2020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9203889/https://doaj.org/toc/2169-3536High-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.Bin LiChenyu LuoZhiyong WangIEEEarticleCharacteristic valueclassification modelcontribution rateGray Wolf algorithmhigh-speed trainindustrial computerElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 173865-173873 (2020)
institution DOAJ
collection DOAJ
language EN
topic Characteristic value
classification model
contribution rate
Gray Wolf algorithm
high-speed train
industrial computer
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
spellingShingle Characteristic value
classification model
contribution rate
Gray Wolf algorithm
high-speed train
industrial computer
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Bin Li
Chenyu Luo
Zhiyong Wang
Application of GWO-SVM Algorithm in Arc Detection of Pantograph
description 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.
format article
author Bin Li
Chenyu Luo
Zhiyong Wang
author_facet Bin Li
Chenyu Luo
Zhiyong Wang
author_sort Bin Li
title Application of GWO-SVM Algorithm in Arc Detection of Pantograph
title_short Application of GWO-SVM Algorithm in Arc Detection of Pantograph
title_full Application of GWO-SVM Algorithm in Arc Detection of Pantograph
title_fullStr Application of GWO-SVM Algorithm in Arc Detection of Pantograph
title_full_unstemmed Application of GWO-SVM Algorithm in Arc Detection of Pantograph
title_sort application of gwo-svm algorithm in arc detection of pantograph
publisher IEEE
publishDate 2020
url https://doaj.org/article/c243c0d53c4d4868bd31cd4b3aab388d
work_keys_str_mv AT binli applicationofgwosvmalgorithminarcdetectionofpantograph
AT chenyuluo applicationofgwosvmalgorithminarcdetectionofpantograph
AT zhiyongwang applicationofgwosvmalgorithminarcdetectionofpantograph
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