Machine learning algorithms for power transformers technical state assessment

Alexandra Khalyasmaa, Stanislav A. Eroshenko, Valeriy Tashchilin, Clement Seguin, Lucas Ehlinger, Rohit Rajendra Vibhute, Saikumar Reddy Atluri

科研成果: Conference contribution

摘要

The paper addresses the problem of operated power transformers actual technical state identification by using existing technical diagnostics retrospective data. The initial data set was composed of results of transformer oil analysis, loading conditions, infrared snapshots and aggregated characteristics of the technical state of bushing, surge arrester and cooling system. Retrospective data of technical diagnostics was used. Technical state estimation process was divided on several steps and was performed by Python. First step was feature selection, where the features with low meaning in terms of the problem formulation are ejected. The second step is the processing of missing data to increase the dataset. KNN algorithm was used to restore missing values. The final step is learning transformer technical state classifier based on random forest tree approach. The results of power transformer state's classification demonstrated relatively high accuracy of identification.

源语言English
主期刊名SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
601-606
页数6
ISBN(电子版)9781728144016
DOI
Published - 十月 2019
活动2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 - Novosibirsk, Russian Federation
期限: 21 十月 201927 十月 2019

出版系列

姓名SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings

Conference

Conference2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
国家Russian Federation
Novosibirsk
时期21/10/201927/10/2019

ASJC Scopus subject areas

  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Instrumentation
  • Computer Networks and Communications

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    引用此

    Khalyasmaa, A., Eroshenko, S. A., Tashchilin, V., Seguin, C., Ehlinger, L., Vibhute, R. R., & Atluri, S. R. (2019). Machine learning algorithms for power transformers technical state assessment. 在 SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings (页码 601-606). [8958395] (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIBIRCON48586.2019.8958395