An Intelligent Transformer Warning Model based on Data-driven Bagging Decision Tree

Hanshen Li, Zhe Li, Huijuan Hou, Gehao Sheng, Xiuchen Jiang, Jinglu Hu

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Transformer is one of the important electrical equipment in power system. Its running state directly affects safe operation of the system. Transformer fault prediction and intelligent early warning are the basis for ensuring normal operation and maintenance of the system. In view of transformer differentiation and the scarcity of fault data, an intelligent transformer warning model based on data-driven bagging decision tree is proposed. A standardized early warning method is proposed based on different transformer equipment condition, and data-driven feature selection for tradeoff is carried out, which provides previous stage knowledge for fault diagnosis. Assessment results depending on fault datasets from State Grid Corporation of China demonstrate that the proposed model can provide a higher accuracy prediction compared to several other prediction models including neural network, KNN, SVM, Linear Discriminant and Logistic Regression, which could bring additional economic benefits and extensive social advantages.

本文言語English
ホスト出版物のタイトル2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings
編集者Ahmed Abu-Siada
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781538641262
DOI
出版ステータスPublished - 2018 11 14
イベント7th International Conference on Condition Monitoring and Diagnosis, CMD 2018 - Perth, Australia
継続期間: 2018 9 232018 9 26

出版物シリーズ

名前2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings

Other

Other7th International Conference on Condition Monitoring and Diagnosis, CMD 2018
CountryAustralia
CityPerth
Period18/9/2318/9/26

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Energy Engineering and Power Technology
  • Safety, Risk, Reliability and Quality

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