Prognostics & health management oriented data analytics suite for transformer health monitoring

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Abstract

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety, and economic effects. With the advance of machine learning techniques, it is possible to enhance traditional transformer health monitoring techniques with data-driven and expert-based prognostics and health management (PHM) applications. Accordingly, this paper reviews the experience of the authors in the implementation of machine learning methods for transformer condition monitoring.
Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalTransformers Magazine
Publication statusPublished - 28 Nov 2022

Keywords

  • machine learning
  • data analytics
  • transformer health monitoring
  • anomaly detection
  • diagnostics
  • prognostics

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