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

Hanshen Li, Zhe Li, Huijuan Hou, Gehao Sheng, Xiuchen Jiang, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings
EditorsAhmed Abu-Siada
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538641262
DOIs
Publication statusPublished - 2018 Nov 14
Event7th International Conference on Condition Monitoring and Diagnosis, CMD 2018 - Perth, Australia
Duration: 2018 Sep 232018 Sep 26

Other

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

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Decision trees
Failure analysis
Logistics
Feature extraction
Neural networks
Economics
Industry

Keywords

  • Bagging Decision Tree
  • Data-driven
  • Feature Selection for tradeoff
  • Intelligent Transformer Warning Model

ASJC Scopus subject areas

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

Cite this

Li, H., Li, Z., Hou, H., Sheng, G., Jiang, X., & Furuzuki, T. (2018). An Intelligent Transformer Warning Model based on Data-driven Bagging Decision Tree. In A. Abu-Siada (Ed.), 2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings [8535665] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CMD.2018.8535665

An Intelligent Transformer Warning Model based on Data-driven Bagging Decision Tree. / Li, Hanshen; Li, Zhe; Hou, Huijuan; Sheng, Gehao; Jiang, Xiuchen; Furuzuki, Takayuki.

2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings. ed. / Ahmed Abu-Siada. Institute of Electrical and Electronics Engineers Inc., 2018. 8535665.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, H, Li, Z, Hou, H, Sheng, G, Jiang, X & Furuzuki, T 2018, An Intelligent Transformer Warning Model based on Data-driven Bagging Decision Tree. in A Abu-Siada (ed.), 2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings., 8535665, Institute of Electrical and Electronics Engineers Inc., 7th International Conference on Condition Monitoring and Diagnosis, CMD 2018, Perth, Australia, 18/9/23. https://doi.org/10.1109/CMD.2018.8535665
Li H, Li Z, Hou H, Sheng G, Jiang X, Furuzuki T. An Intelligent Transformer Warning Model based on Data-driven Bagging Decision Tree. In Abu-Siada A, editor, 2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8535665 https://doi.org/10.1109/CMD.2018.8535665
Li, Hanshen ; Li, Zhe ; Hou, Huijuan ; Sheng, Gehao ; Jiang, Xiuchen ; Furuzuki, Takayuki. / An Intelligent Transformer Warning Model based on Data-driven Bagging Decision Tree. 2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings. editor / Ahmed Abu-Siada. Institute of Electrical and Electronics Engineers Inc., 2018.
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