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

Resultado de la investigación: Conference contribution

Resumen

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.

Idioma originalEnglish
Título de la publicación alojadaSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas601-606
Número de páginas6
ISBN (versión digital)9781728144016
DOI
EstadoPublished - oct 2019
Evento2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 - Novosibirsk, Russian Federation
Duración: 21 oct 201927 oct 2019

Serie de la publicación

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

Conference

Conference2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
PaísRussian Federation
CiudadNovosibirsk
Período21/10/201927/10/2019

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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    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. En SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings (pp. 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