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

Aizpurua, Jose Ignacio and Stewart, Brian G. and McArthur, Stephen D. J. (2022) Prognostics & health management oriented data analytics suite for transformer health monitoring. Transformers Magazine. pp. 1-9. (https://transformers-magazine.com/magazine/prognos...)

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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.

ORCID iDs

Aizpurua, Jose Ignacio, Stewart, Brian G. and McArthur, Stephen D. J. ORCID logoORCID: https://orcid.org/0000-0003-1312-8874;
CORE (COnnecting REpositories)