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

Jose I. Aizpurua
Brian G. Stewart
Stephen D. J. McArthur

Puni tekst: engleski pdf 1.835 Kb

str. 66-75

preuzimanja: 27



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.

Ključne riječi

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

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