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

Результат исследований: Глава в книге, отчете, сборнике статейМатериалы конференции

Аннотация

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.

Язык оригиналаАнглийский
Название основной публикацииSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы601-606
Число страниц6
ISBN (электронное издание)9781728144016
DOI
СостояниеОпубликовано - окт 2019
Событие2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 - Novosibirsk, Российская Федерация
Продолжительность: 21 окт 201927 окт 2019

Серия публикаций

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

Конференция

Конференция2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
СтранаРоссийская Федерация
ГородNovosibirsk
Период21/10/201927/10/2019

Предметные области ASJC Scopus

  • 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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    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. В SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings (стр. 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