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

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

Abstract

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.

Original languageEnglish
Title of host publicationSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages601-606
Number of pages6
ISBN (Electronic)9781728144016
DOIs
Publication statusPublished - Oct 2019
Event2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 - Novosibirsk, Russian Federation
Duration: 21 Oct 201927 Oct 2019

Publication series

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

Conference

Conference2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
CountryRussian Federation
CityNovosibirsk
Period21/10/201927/10/2019

Keywords

  • classification
  • data filtration and recovery
  • machine learning
  • power transformer
  • technical state

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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